(19)
(11)EP 3 346 758 B1

(12)EUROPEAN PATENT SPECIFICATION

(45)Mention of the grant of the patent:
10.06.2020 Bulletin 2020/24

(21)Application number: 16841715.2

(22)Date of filing:  26.08.2016
(51)Int. Cl.: 
H04W 24/06  (2009.01)
H04W 72/08  (2009.01)
H04W 72/04  (2009.01)
H04B 7/024  (2017.01)
H04W 28/16  (2009.01)
(86)International application number:
PCT/JP2016/074978
(87)International publication number:
WO 2017/038683 (09.03.2017 Gazette  2017/10)

(54)

SCHEDULING DEVICE AND METHOD

PLANUNGSVORRICHTUNG UND VERFAHREN

DISPOSITIF ET PROCÉDÉ DE PLANIFICATION


(84)Designated Contracting States:
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

(30)Priority: 31.08.2015 JP 2015170295

(43)Date of publication of application:
11.07.2018 Bulletin 2018/28

(73)Proprietor: Nippon Telegraph And Telephone Corporation
Chiyoda-ku, Tokyo 100-8116 (JP)

(72)Inventors:
  • ARIKAWA,Yuki
    Musashino-shi Tokyo 180-8585 (JP)
  • UZAWA,Hiroyuki
    Musashino-shi Tokyo 180-8585 (JP)
  • KAWAI,Kenji
    Musashino-shi Tokyo 180-8585 (JP)
  • SHIGEMATSU,Satoshi
    Musashino-shi Tokyo 180-8585 (JP)

(74)Representative: Samson & Partner Patentanwälte mbB 
Widenmayerstraße 6
80538 München
80538 München (DE)


(56)References cited: : 
WO-A1-2010/019613
JP-A- 2010 220 253
JP-A- 2015 111 788
US-A1- 2007 280 175
CN-A- 103 825 677
JP-A- 2011 193 441
JP-A- 2015 111 788
US-A1- 2010 261 482
  
  • YUKI ARIKAWA ET AL.: 'Practical Resource Scheduling in Massive- cell Deployment for 5G Mobile Communications Systems' ISPACS,2015 INTERNATIONAL SYMPOSIUM ON, IEEE, [Online] 20 March 1113, XP032881212 Retrieved from the Internet: <URL:http://ieeexplore.org/stamp/stamp.jsp? tp= &arnumber=7432815>
  
Note: Within nine months from the publication of the mention of the grant of the European patent, any person may give notice to the European Patent Office of opposition to the European patent granted. Notice of opposition shall be filed in a written reasoned statement. It shall not be deemed to have been filed until the opposition fee has been paid. (Art. 99(1) European Patent Convention).


Description

Technical Field



[0001] The present invention relates to a radio network control technique and more particularly to a scheduling technique for allocating the radio resources of a radio network by designating the operation contents (transmission state) of each transmission point in the radio network.

Background Art



[0002] In the field of radio networks, high-density arrangement of small cells to accommodate rapidly increasing mobile traffic has been investigated. Small cells have a smaller cell radius as compared to a macro cell, and operate with lower transmission power from a base station (transmission point: TP). Thus, the number of mobile terminals (User Equipments: UEs) sharing the same frequency within the cell can be reduced, and a per-terminal throughput can be improved.

[0003] However, high-density arrangement of small cells prompt greater interference power from an adjacent cell. For example, a case in which a plurality of TPs simultaneously transmits data to different UEs using the same frequency band is considered. In this case, for each UE, transmission signals from TPs other than a TP that transmits the data destined for the UE cause interference power to a desired reception signal, thereby rather decreasing the throughput.

[0004] To solve this problem, a next-generation radio communication interface such as LTE (Long Term Evolution)/LTE-A adopts CoMP (Coordinated Multi-Point transmission/reception) scheduling to suppress interference power between cells within the same frequency band (non-patent literature 1). CoMP scheduling schedules TP operation contents (transmission destination UE/transmission stop) within the same frequency band.

[0005] More specifically, a plurality of transmission point-user equipment combination patterns are evaluated using a predetermined evaluation function, and a pattern having a highest evaluation value is searched for, and is selected/output as an optimum transmission pattern.

[0006] In examples of a combination pattern shown in Fig. 14, for example, in pattern #1, UE0, UE5, UE8,... are assigned as transmission destinations of TP1, TP2, TP3,... When no transmission destination is assigned and a transmission stop is set, "Blank" is written, like TP3 of pattern #2.

[0007] In addition, the predetermined evaluation function indicates the sum of TP-specific lower evaluation values calculated based on external evaluation information that is input externally, and each lower evaluation value is a value obtained by dividing the instantaneous throughput of the transmission destination UE of the corresponding TP by an average throughput based on a proportional-fairness method (non-patent literature 2). The external evaluation information at this time represents, for example, the average throughput of each UE, the untransmitted data amount of each UE, and the TP-specific channel quality state of each UE. The channel quality state is indicated by, for example, CQI (Channel Quality Indicator) fed back from the UE (non-patent literatures 3 and 4).

[0008] To efficiently search for a combination pattern having the highest evaluation value, a method that applies a hill-climbing method to CoMP scheduling can be considered. The hill-climbing method is a search algorithm of repeating a small correction as many times as possible to obtain a desired pattern having the highest evaluation value. In this search, a small correction is a correction of the transmission destination of one of the TPs of the combination pattern to improve the evaluation value.

[0009] As shown in Fig. 15, a scheduling apparatus 50 includes a pattern generation unit 51 for generating transmission point-user equipment combination patterns, a pattern evaluation unit 52 for calculating the evaluation value of each of the generated patterns using an evaluation function, a transmission pattern selection unit 53 for holding the pattern having the highest evaluation value among the generated patterns, and an end determination unit 54 for detecting that the upper limit of an evaluation count is reached, and externally outputting, as an optimum transmission pattern, the pattern having the highest evaluation value among the generated patterns. The functions of the respective units will be described in detail below.

<Pattern Generation Unit>



[0010] The pattern generation unit 51 changes the transmission destination of only one of the TPs of the transmission pattern input from a transmission pattern selection unit (to be described later), and then outputs the changed pattern.

[0011] As shown in Fig. 16, upon receiving a start instruction from the outside of the scheduling apparatus 50, the pattern generation unit 51 performs an initialization process. In this initialization process, the selected flags of all the TPs are cleared to 0, the presence/absence of trial of each of the transmission destinations of all the TPs is cleared, and the transmission destinations of all the TPs in the internally held pattern are rewritten to indicate a transmission stop (Blank).

[0012] After the initialization process, a single TP (S_TP) in which the transmission destination is to be changed is randomly selected. Note that the TP to be selected is a TP with the selected flag "0". Next, the transmission destination of S_TP in the internally held pattern is changed. This transmission destination is a UE in which presence/absence of trial of the transmission destination indicates "absence" in the transmission destination UE list of S_TP among TP-specific transmission destination UE lists input from the outside of the scheduling apparatus 50. Along with the change processing, the presence/absence of trial of the transmission destination is updated to the status of "presence". When the presence/absence of trial of each of all the UEs of the transmission destination UE list of S_TP indicates "presence", another TP is reselected as S_TP, and the same transmission destination change process is performed. The internally held pattern that has been changed is output from the pattern generation unit 51. After the pattern generation processing, the pattern generation unit 51 waits for an input from the transmission pattern selection unit 53. Upon detecting the input, the input transmission pattern is set as the internally held pattern, and the same change process is performed.

<Pattern Evaluation Unit>



[0013] The pattern evaluation unit 52 calculates, using the evaluation function, the evaluation value of the pattern input from the pattern generation unit 51. The calculated evaluation value and the evaluated pattern are output to the transmission pattern selection unit 53.

<Transmission Pattern Selection Unit>



[0014] The transmission pattern selection unit 53 selects/outputs, as a transmission pattern, the pattern having the highest evaluation value among the generated evaluated patterns.

[0015] As shown in Fig. 17, upon receiving a start instruction from the outside of the scheduling apparatus 50, the transmission pattern selection unit 53 performs an initialization process. In this initialization process, an internally held evaluation value is set to 0, and the transmission destinations of all the TPs of the internally held pattern are rewritten to indicate a transmission stop (Blank).

[0016] After the initialization process, when the evaluated pattern is input from the pattern evaluation unit 52, it is confirmed whether the evaluation value that is input together with the evaluated pattern exceeds the internally held evaluation value. When the input evaluation value exceeds the internally held evaluation value, which indicates that the input evaluated pattern is better, and thus the internally held pattern and internally held evaluation value are updated to the evaluated pattern and evaluation value; otherwise, the internally held pattern and internally held evaluation value are maintained. After the update process, the transmission pattern selection unit 53 outputs the internally held pattern and internally held evaluation value to the end determination unit 54.

<End Determination Unit>



[0017] The end determination unit 54 counts the evaluation count of the evaluated patterns. When the count value reaches the upper limit (end condition) of the evaluation count input from the outside of the scheduling apparatus 50, the end determination unit 54 determines the end of the search, sets the end flag to 1, and outputs, as an optimum transmission pattern (scheduling result), the transmission pattern output from the transmission pattern selection unit 53 at this time. Note that the end flag and the count value of the evaluation count are initialized to 0 every time a start instruction is received.

[0018] By using the scheduling apparatus 50, a transmission pattern output from the transmission pattern selection unit 53 has an evaluation value which is continuously and monotonously improved as the evaluation is repeated, as shown in Fig. 18. By this, the transmission pattern can converge to a pattern that has a high evaluation value after a sufficient numbers of evaluations are repeated.

[0019] The scheduling apparatus 50 selects a transmission pattern corresponding to the thus obtained convergence value, and outputs it as a scheduling result.

[0020] In this arrangement example, however, when selecting a transmission pattern from generated patterns, the hill-climbing method is used as a search algorithm to search for an extreme value of the evaluation values of the patterns. Therefore, even if the convergence value is not the maximum value but a relatively low extreme value, the initially found extreme value is unfavorably selected as a final convergence value, thereby ending the subsequent search. Accordingly, there is a problem that a pattern corresponding to the relatively low convergence value is selected as an optimum transmission pattern, that is, a scheduling result.

Related Art Literature


Non-Patent Literature



[0021] 

Non-Patent Literature 1: Taoka, et al., "MIMO and inter-cell cooperative transmission and reception technology in LTE-Advanced", NTT DOCOMO Technical Journal, Vol. 18, No. 2, Jul. 2010

Non-Patent Literature 2: Tolga Giri, et al., "Proportional Fair Scheduling Algorithm in OFDMA-Based Wireless Systems with QoS Constraints", JOURNAL OF COMMUNICATIONS AND NETWORKS, Vol. 12, No. 1, FEBRUARY 2010

Non-Patent Literature 3: Sandy Fraser, "LTE Channel State Information (CSI)", http://www.keysight.com/upload/cmc_upload/All/31May2012_ LTE.pdf?&cc=JP&lc=jpn

Non-Patent Literature4: 3GPP, TS 36.213 (V.8.2.0)
A further example is known from JP 2015-111788 A, which relates to a wireless communication system and method for selecting a plurality of base stations that performs cooperative communication based on predetermined conditions, such as the sum of interference electric powers.


Disclosure of Invention


Problem to be Solved by the Invention



[0022] The present invention has been made in view of the above-described problem, and an object of the present invention is to provide a scheduling technique capable of searching an optimum transmission pattern that has a higher evaluation value.

Means of Solution to the Problem



[0023] The invention is set out in the appended set of claims. In order to achieve the object of the present invention, there is provided a scheduling apparatus and a scheduling method according to the independent claims. In particular, the scheduling apparatus generates a plurality of patterns each indicating combinations of a plurality of transmission points forming a radio network system and user equipments for performing radio communication with the transmission points, and the scheduling apparatus that selects an optimum transmission pattern indicating optimum combinations of transmission points and user equipments based on evaluation values of the patterns by a predetermined search algorithm, the apparatus comprising a convergence pattern selection unit configured to generate sequentially a plurality of different patterns based on designated initial conditions, to select, as a convergence pattern, the pattern in which evaluation value has converged to an extreme value, and repeatedly execute selection of the convergence pattern by changing the initial conditions every time the convergence pattern is selected, and a transmission pattern determination unit configured to select, as the optimum transmission pattern, one of the convergence patterns with the highest evaluation value obtained by the convergence pattern selection unit.

[0024] There is also provided a scheduling method of generating a plurality of patterns each indicating combinations of a plurality of transmission points forming a radio network system and user equipments for performing radio communication with the transmission points, and selecting an optimum transmission pattern indicating optimum combinations of transmission points and user equipments based on evaluation values of the patterns by a predetermined search algorithm, the method comprising a convergence pattern selection step of sequentially generating a plurality of different patterns based on designated initial conditions, selecting, as a convergence pattern, the pattern in which evaluation value has converged to an extreme value, and repeatedly executing selection of the convergence pattern by changing the initial conditions every time the convergence pattern is selected, and a transmission pattern determination step of selecting, as the optimum transmission pattern, one of the convergence patterns with the highest evaluation value obtained in the convergence pattern selection step.

Effect of the Invention



[0025] According to the present invention, every time a pattern in which an evaluation value has converged to an extreme value is selected as a convergence pattern, a convergence pattern selection is repeatedly executed for a plurality of times by changing initial conditions that are used to generate a pattern, thereby selecting one of the obtained convergence patterns which has the highest evaluation value as an optimum transmission pattern. Therefore, as compared to a case in which an initially found extreme value is selected as a final convergence value, and ending the subsequent search, it is possible to select a pattern having a higher convergence value as an optimum transmission pattern.

[0026] In a scheduling process, a scheduling period is defined, and thus, there is an upper limit of an evaluation count. So that it is necessary to appropriately distribute the evaluation count in conducting each search. According to this embodiment, since a new search starts after convergence is detected, it is possible to avoid a situation in which the evaluation count is insufficient in conducting one search, and executing a next search before reaching a convergence. Furthermore, starting a new search after detecting a convergence is equivalent to distributing a necessary and sufficient evaluation count in each of the searches, thereby making it possible to efficiently use the limited evaluation count.

Brief Description of Drawings



[0027] 

Fig. 1 is a block diagram showing the arrangement of a scheduling apparatus according to the first embodiment;

Fig. 2 is a flowchart illustrating pattern generation processing according to the first embodiment;

Fig. 3 is a flowchart illustrating candidate pattern selection processing according to the first embodiment;

Fig. 4 is a flowchart illustrating convergence determination processing according to the first embodiment;

Fig. 5 is a flowchart illustrating transmission pattern selection processing according to the first embodiment;

Fig. 6 is a block diagram showing the arrangement of a scheduling apparatus according to the second embodiment;

Fig. 7 is a flowchart illustrating initial condition generation processing according to the second embodiment;

Fig. 8 is a block diagram showing the arrangement of a scheduling apparatus according to the third embodiment;

Fig. 9 is an explanatory view showing transition of a pattern evaluation value;

Fig. 10 is a flowchart illustrating convergence determination processing according to the third embodiment;

Fig. 11 is a flowchart illustrating the convergence determination processing (continued) according to the third embodiment;

Fig. 12 is timing chart for explaining the relationship between the evaluation value and an elapsed time according to the third embodiment;

Fig. 13 is a block diagram showing the arrangement of a scheduling apparatus according to the fourth embodiment;

Fig. 14 is a table showing examples of a transmission point-user equipment combination pattern;

Fig. 15 is a block diagram showing an example of the arrangement of a scheduling apparatus;

Fig. 16 is a flowchart illustrating pattern generation processing shown in Fig. 15;

Fig. 17 is a flowchart illustrating transmission pattern selection processing shown in Fig. 15; and

Fig. 18 is a graph showing transition of a pattern evaluation value.


Best Mode for Carrying Out the Invention



[0028] Embodiments of the present invention will be described next with reference to the accompanying drawings.

[First Embodiment]



[0029] A scheduling apparatus 10 according to the first embodiment of the present invention will be described first with reference to Fig. 1.

[0030] This scheduling apparatus 10 is an apparatus for allocating the radio resources of a radio network, and has a function of executing CoMP scheduling for calculating an evaluation value for each of a plurality of transmission point-user equipment combination patterns, and selecting an optimum pattern based on the obtained evaluation values to suppress interference power between cells within the same frequency band.

[0031] As shown in Fig. 1, the scheduling apparatus 10 according to this embodiment is provided with an initial condition generation unit 11, a pattern generation unit 12, a pattern evaluation unit 13, a candidate pattern selection unit 14, a convergence determination unit 15, a transmission pattern selection unit 16, and an end determination unit 17 as main functional units. As compared to the above-described arrangement shown in Fig. 15, the initial condition generation unit 11, the candidate pattern selection unit 14, and the convergence determination unit 15 are newly provided.

[0032] Among these functional units, the initial condition generation unit 11, the pattern generation unit 12, the pattern evaluation unit 13, the candidate pattern selection unit 14, and the convergence determination unit 15 form a convergence pattern selection unit 10A. That is, the convergence pattern selection unit 10A has a function of sequentially generating a plurality of different patterns based on designated initial conditions, and selecting, as a convergence pattern, a pattern in which an evaluation value has converged to an extreme value by applying the hill-climbing method as a search algorithm to the generated patterns, and a function of repeatedly executing selection of the convergence pattern a plurality of times by changing the initial conditions every time the convergence pattern is selected.

[0033] Among the functional units, the transmission pattern selection unit 16 and the end determination unit 17 form a transmission pattern determination unit 10B. That is, the transmission pattern determination unit 10B has a function of selecting, as an optimum transmission pattern, one of the convergence patterns obtained by the convergence pattern selection unit 10A, which has the highest evaluation value.

[0034] In the present invention, a candidate pattern indicates a pattern having an evaluation value higher than that of a previous pattern in a search based on the same initial conditions, and the convergence pattern indicates a pattern obtained when the evaluation value converges to the highest extreme value in a search based on the same initial conditions. The convergence pattern is the same as the candidate pattern before the evaluation value converges in the searches based on the same initial conditions. A transmission pattern indicates a pattern having an evaluation value higher than that of a previous convergence pattern, among convergence patterns obtained in searches based on different initial conditions. The optimum transmission pattern indicates a transmission pattern obtained when an evaluation count for patterns generated in each search reaches the upper limit.

[0035] The functions and processing operations of the functional units of the scheduling apparatus 10 according to this embodiment will be described in detail next.

<Initial Condition Generation Unit>



[0036] The initial condition generation unit 11 has a function of generating and outputting an initial pattern and a seed value to be used to generate a pattern in the pattern generation unit 12.

[0037] More specifically, the initial condition generation unit 11 has, as a function associated with generation of an initial pattern, a function of generating and outputting an initial pattern in which a transmission stop is set for all transmission points, when outputting an initial pattern first.

[0038] The initial condition generation unit 11 has, as functions associated with generation of a seed value, a function of generating and outputting a seed value to be used to generate a pattern when outputting an initial pattern for the first time, and a function of generating and outputting a seed value having a unique value every time the convergence determiner 15 detects a convergence. Note that when performing the generating process at the time the convergence is detected by the convergence determination unit 15, in order to set initial conditions different from those in the initial search, a unique value, that is, a value which has not been generated from when the search starts until the generating process is set as the seed value.

[0039] In the initial condition generation unit 11, a timing of generating the initial pattern and seed value as the initial conditions is immediately after a start instruction input from the outside of the scheduling apparatus 10 is received, and when the convergence determination unit 15 detects a convergence. That is, when a convergence flag as a signal indicating completion of convergence is set to 1, only the seed value is changed.

[0040] Note that the present invention incorporates a method of changing one or both of the initial pattern and the seed value as a method of changing the initial conditions when the convergence determination unit 15 detects a convergence.

[0041] This embodiment will exemplify a case in which a search is repeatedly executed by changing the seed value when the convergence determination unit 15 detects a convergence, and using the same initial pattern. The second embodiment (to be described later) will exemplify a case in which a search is repeatedly executed by changing both the initial pattern and the seed value when the convergence determination unit 15 detects convergence.

<Pattern Generation Unit>



[0042] The pattern generation unit 12 has a function of generating a plurality of different patterns based on the initial pattern and seed value output from the initial condition generation unit 11, and sequentially outputting the generated patterns.

[0043] More specifically, the pattern generation unit 12 has, as functions associated with pattern generation, a function of selecting, as a change target transmission point, one of the transmission points of the initial pattern output from the initial condition generation unit 11 at the start of pattern generation, generating a pattern by changing a user equipment as a transmission destination only for the change target transmission point, and outputting the generated pattern, and a function of, every time the convergence determination unit 15 outputs the convergence pattern, selecting, as a change target point, one of the transmission points of the convergence pattern, generating a pattern by changing a user equipment as a transmission destination only for the change target transmission point, and outputting the generated pattern.

[0044] The pattern generation unit 12 has, as a function of selecting a change target point, a function of selecting the change target transmission point based on a random number generated from the seed value output from the initial condition generation unit 11.

[0045] The pattern generation unit 12 is basically the same as that shown in Fig. 15 described above except that the pattern generation unit 12 starts generation from the initial pattern from the initial condition generation unit 11, and except that the pattern generation unit 12 returns the state back to the initial state when the convergence flag as a signal, that is input from the convergence determination unit 15, shows 1 indicating completion of convergence.

[0046] In a flowchart illustrating pattern generating process shown in Fig. 2, upon receiving a start instruction from the outside of the scheduling apparatus 10, the pattern generation unit 12 initializes the process (step 100). In this initialization process, the selected flags of all the TPs are cleared to 0, and the presence/absence of trial of each of the transmission destinations of all the TPs is cleared.

[0047] Subsequently, the pattern generation unit 12 receives the initial conditions from the initial condition generation unit 11 (step 101), sets the received initial pattern as an internally held pattern (step 102), and sets the received seed value as a random number generation seed value (step 103).

[0048] The pattern generation unit 12 confirms the selected flags of all the TPs (step 104). When not all the selected flags of the TPs are 1 (selected) (NO in step 104), one of the TPs is randomly selected as a change target point S_TP based on a random number generated from the seed value (step 106); otherwise (YES in step 104), all the selected flags of the TPs are cleared to 0, and the presence/absence of trial of each of the transmission destinations of all the TPs is cleared (step 105). Then, the process advances to step 106.

[0049] The pattern generation unit 12 confirms the presence/absence of trial of each of the transmission destinations described in the transmission destination UE list of S_TP among TP-specific transmission destination UE lists input from the outside of the scheduling apparatus 50 (step 107). When all the transmission destinations have been tried (YES in step 107), the selected flag of S_TP is set to 1 (step 108), and the process returns to step 106.

[0050] On the other hand, when not all the transmission destinations have been tried (NO in step 107), the pattern generation unit 12 randomly selects one of the UEs in which presence/absence of trial of the transmission destination indicates "absence" in the transmission destination UE list of S_TP, sets the selected UE as a new transmission destination of S_TP of the internally held pattern, and sets the presence/absence of trial of the selected UE to "presence" (step 109). The pattern generation unit 12 sequentially generates a new pattern by changing some of TP-UE combinations of the internally held pattern, in this example, only a combination associated with S_TP, and outputs the generated pattern to the pattern evaluation unit 13 (step 110).

[0051] After that, the pattern generation unit 12 receives the convergence pattern and convergence flag from the convergence determination unit 15 (step 111), and confirms the convergence flag (step 112). When the convergence flag = 1 and a convergence of the evaluation value has been detected (YES in step 112), the process returns to step 100 and new pattern generation based on different initial conditions starts to perform a new search by the hill-climbing method.

[0052] On the other hand, when the convergence flag = 0 and no convergence of the evaluation value has been detected (NO in step 112), the pattern generation unit 12 sets the received convergence pattern as the internally held pattern (step 113), and returns to step 104 to start pattern generation based on the received convergence pattern. This allows the search in progress to be continuously performed by the hill-climbing method.

<Pattern Evaluation Unit>



[0053] The pattern evaluation unit 13 has a function of sequentially calculating, every time the pattern generation unit 12 outputs a pattern, the evaluation value of the pattern, and a function of outputting the evaluated pattern and the calculated evaluation value to the candidate pattern selection unit 14.

[0054] The pattern evaluation unit 13 is basically the same as that shown in Fig. 15 described above.

[0055] The evaluation function indicates the sum of TP-specific lower evaluation values calculated based on external evaluation information input from the outside of the scheduling apparatus 10, and each lower evaluation value is a value obtained by dividing the instantaneous throughput of the transmission destination UE of the corresponding TP by an average throughput based on a proportional-fairness method (non-patent literature 2). The external evaluation information at this time represents, for example, the average throughput of each UE, the untransmitted data amount of each UE, and the TP-specific channel quality state of each UE. The channel quality state is indicated by, for example, CQI (Channel Quality Indicator) fed back from the UE (non-patent literatures 3 and 4).

<Candidate Pattern Selection Unit>



[0056] The candidate pattern selection unit 14 has a function of comparing, every time the pattern generation unit 12 outputs a pattern, the evaluation value of the pattern with that of the candidate pattern, sequentially selecting, as a new candidate pattern, the pattern having the higher evaluation value, and sequentially outputting the selected pattern.

[0057] The candidate pattern selection unit 14 is basically the same as that shown in Fig. 15 described above except that the candidate pattern selection unit 14 returns the selected state of the candidate pattern to the initial state when the convergence flag output from the convergence determination unit 15 is 1.

[0058] In a flowchart illustrating candidate pattern selecting process shown in Fig. 3, upon receiving a start instruction from the outside of the scheduling apparatus 10, the candidate pattern selection unit 14 initializes the process (step 120). In this initialization process, the internally held evaluation value is set to 0 and the transmission destinations of all the TPs of the internally held pattern are rewritten to indicate a transmission stop (Blank).

[0059] The candidate pattern selection unit 14 receives the evaluated pattern from the pattern evaluation unit 13 together with the evaluation value (step 121), and compares the received evaluation value with the internally held evaluation value (step 122). When the received evaluation value is equal to or smaller than the internally held evaluation value (NO in step 122), the candidate pattern selection unit 14 outputs the candidate pattern including the internally held pattern and the evaluation value to the convergence determination unit 15 (step 124).

[0060] On the other hand, when the received evaluation value is larger than the internally held evaluation value (YES in step 122), the candidate pattern selection unit 14 sets the received evaluated pattern and evaluation value as the internally held pattern and internally held evaluation value (step 123), and advances to step 124.

[0061] After that, the candidate pattern selection unit 14 stands by until a convergence flag indicating a convergence determination result output from the convergence determination unit 15 is received in correspondence with the output candidate pattern (step 125), and confirms the received convergence flag (step 126). When the convergence flag = 1 and a convergence of the evaluation value has been detected (YES in step 126), the process returns to step 120 and evaluation of a new pattern generated based on different initial conditions starts to perform a new search by the hill-climbing method. Note that the convergence flag may be confirmed after a predetermined time elapses since the candidate pattern is output. The predetermined time is, for example, a time required from when the convergence determination unit 15 executes convergence determination process corresponding to the output candidate pattern until the candidate pattern selection unit 14 is notified of the result (convergence flag).

[0062] On the other hand, when the convergence flag = 0 and no convergence of the evaluation value has been detected (NO in step 126), the candidate pattern selection unit 14 returns to step 104 to start pattern generation based on the received convergence pattern. This allows the search in progress to be continued by the hill-climbing method.

<Convergence Determination Unit>



[0063] The convergence determination unit 15 has a function of selecting, when it is detected that the evaluation value of the candidate pattern output from the candidate pattern selection unit 14 has converged to a predetermined extreme value, the candidate pattern as a convergence pattern, and a function of instructing the initial condition generation unit 11 to generate/output a different initial pattern, instructing the pattern generation unit 12 to perform initialization and newly start pattern generation, and instructing the candidate pattern selection unit 14 to initialize the candidate pattern.

[0064] In a flowchart illustrating convergence determination process shown in Fig. 4, upon receiving a start instruction from the outside of the scheduling apparatus 10, the convergence determination unit 15 initializes the processing (step 130). In this initialization process, the convergence flag is cleared to 0, and a current internally held evaluation value, a last internally held evaluation value, a convergence determination count value, and a convergence determination interval count value are respectively set to 0.

[0065] Subsequently, the convergence determination unit 15 receives the candidate pattern and evaluation value from the candidate pattern selection unit 14 (step 131), and sets the received candidate pattern and evaluation value as the internally held pattern and the current internally held evaluation value (step 132).

[0066] The convergence determination unit 15 increments (+1) the convergence determination interval count value (step 133), and compares the convergence determination interval count value with a convergence determination interval (step 134).

[0067] When the convergence determination interval count value is equal to or larger than the convergence determination interval (YES in step 134), the convergence determination unit 15 clears the convergence determination interval count value to 0 (step 135), and calculates a difference of the evaluation values by subtracting the last internally held evaluation value from the current internally held evaluation value (step 136) .

[0068] After that, the convergence determination unit 15 compares the difference of the evaluation values with a convergence determination threshold (step 137). When the difference of the evaluation values is equal to or smaller than the convergence determination threshold (YES in step 137), which indicates a change in evaluation value is small and there is a convergence tendency, and thus the convergence determination unit 15 increments the convergence determination count value (step 139), and compares the convergence determination count value with a convergence determination count (step 140) .

[0069] When the convergence determination count value has reached the convergence determination count (YES in step 140), the convergence determination unit 15 determines that the evaluation value has converged to the predetermined extreme value as the convergence tendency has continued up to a predetermined count, and sets the convergence flag to 1 (step 141).

[0070] After that, the convergence determination unit 15 sets the current internally held evaluation value as the last internally held evaluation value (step 142), outputs the convergence flag to the initial condition generation unit 11, the pattern generation unit 12, and the candidate pattern selection unit 14, outputs the convergence pattern including the current internally held evaluation value to the pattern generation unit 12 and the transmission pattern selection unit 16, and outputs the evaluation value to the transmission pattern selection unit 16 (step 143).

[0071] Note that when it is determined in step 134 that the convergence determination interval count value is smaller than the convergence determination interval (NO in step 134), and when it is determined in step 140 that the convergence determination count value has not reached the convergence determination count (NO in step 140), the process advances to step 143.

[0072] When it is determined in step 137 that the difference evaluation value is larger than the convergence determination threshold (NO in step 137), which indicates that a change in evaluation value is large and there is no convergence tendency, and thus the convergence determination unit 15 sets the convergence determination count value to 0 (step 138), and advances to step 143.

[0073] After that, the convergence determination unit 15 confirms the convergence flag (step 144). When the convergence flag = 1 and a convergence of the evaluation value has been detected (YES in step 144), the process returns to step 130 and convergence determination for a new pattern generated based on different initial conditions starts to perform a new search by the hill-climbing method.

[0074] On the other hand, when the convergence flag = 0 and no convergence of the evaluation value has been detected (NO in step 144), the process returns to step 131 to start convergence determination for the candidate pattern and evaluation value newly output from the candidate pattern selection unit 14. This continues the search in progress to be continued by the hill-climbing method.

<Transmission Pattern Selection Unit>



[0075] The transmission pattern selection unit 16 has a function of selecting, as a transmission pattern, one of the convergence patterns selected by the convergence determination unit 15, which has the highest evaluation value, and outputting the transmission pattern to the end determination unit 17.

[0076] In a flowchart illustrating transmission pattern selection processing shown in Fig. 5, upon receiving a start instruction from the outside of the scheduling apparatus 10, the transmission pattern selection unit 16 initializes the process (step 150). In this initialization process the internally held evaluation value is set to 0 and the transmission destinations of all the TPs of the internally held pattern are rewritten to indicate a transmission stop (Blank).

[0077] Subsequently, the transmission pattern selection unit 16 receives the convergence pattern from the convergence determination unit 15 together with the evaluation value (step 151), and compares the received evaluation value with the internally held evaluation value (step 152). When the received evaluation value is equal to or smaller than the internally held evaluation value (NO in step 152), the transmission pattern selection unit 16 outputs the internally held pattern as a transmission pattern to the end determination unit 17 (step 154), and returns to step 151.

[0078] On the other hand, when the received evaluation value is larger than the internally held evaluation value (YES in step 152), the received convergence pattern and evaluation value are set as the internally held pattern and internally held evaluation value (step 153), and the process advances to step 154.

<End Determination Unit>



[0079] The end determination unit 17 has a function of selecting, when the evaluation count for the patterns generated by the pattern generation unit 12 reaches the upper limit of the evaluation count that is input from the outside of the scheduling apparatus 10, the transmission pattern selected by the transmission pattern selection unit 16 as an optimum transmission pattern, and a function of outputting the optimum transmission pattern, that is, outputting a scheduling result and the end flag = 1 to the outside of the scheduling apparatus 10. The end determination unit 17 is the same as that shown in Fig. 15 described above.

[0080] Note that a search algorithm for searching for a convergence pattern does not necessarily have to be the hill-climbing method. For example, it is possible to apply well-known solutions to a combinatorial optimization problem, such as a greedy algorithm of repeating pattern generation until an evaluation value increases within a cycle, a branch and cut method of generating a pattern so as to satisfy constraints after dividing patterns into groups in accordance with a difference in number of transmission points for which a transmission stop is preset and determining, based on the representative evaluation values of the groups, a group that is meticulously searched, and dynamic programming for holding two or more patterns at the same time by leaving two or more user equipment candidates as transmission destinations.

[0081] The above embodiment has exemplified the example of selecting one transmission point as a change target point. However, the number of transmission points selected as change target points need not be one. For example, two or more transmission points may be selected as change target points, and a pattern in which user equipments as the transmission destinations of two transmission points are simultaneously changed is generated. Even if two or more transmission points that are irrelevant in terms of the radio wave state between the transmission points are selected based on the radio wave state between the transmission points perceived by measurement or estimation, it is possible to simultaneously select user equipments as the transmission destinations of the two transmission points without influencing each other, thereby obtaining an effect of shortening the time until user equipments as the transmission destinations of all the transmission points are selected.

[0082] The above embodiment has exemplified the example in which every time a user equipment as the transmission destination of the selected transmission point is determined, the pattern is output as a candidate pattern. However, the timing of reflecting the determination of the user equipment as the transmission destination on the candidate pattern is not limited to this. For example, when user equipments as the transmission destinations of all the transmission points are determined, that is, when selection of all the transmission points is completed, this may be reflected on the candidate pattern and then the pattern may be output.

[Effect of First Embodiment]



[0083] As described above, in this embodiment, the convergence pattern selection unit 10A sequentially generates a plurality of different patterns based on designated initial conditions, selects, as a convergence pattern, a pattern whose evaluation value has converged to an extreme value, and repeatedly executes selection of the convergence pattern by changing the initial conditions every time the convergence pattern is selected, and the transmission pattern determination unit 10B selects, as an optimum transmission pattern, one of the convergence patterns obtained by the convergence pattern selection unit 10A, which has the highest evaluation value.

[0084] More specifically, in the convergence pattern selection unit 10A, the initial condition generation unit 11 generates and outputs an initial pattern and a seed value as initial conditions to be used to generate a pattern, the pattern generation unit 12 generates, from the initial pattern, a plurality of different patterns by sequentially changing some of combinations selected based on a random number generated from the seed value, and sequentially outputs the generated patterns, the pattern evaluation unit 13 sequentially calculates, every time the pattern generation unit 12 outputs the pattern, the evaluation value of the pattern, the candidate pattern selection unit 14 compares, every time the pattern generation unit 12 outputs the pattern, the evaluation value of the pattern with that of a candidate pattern, selects, as a new candidate pattern, the pattern having the higher evaluation value, and sequentially outputs the selected pattern, and the convergence determination unit 15 selects, when it is detected that the evaluation value of the candidate pattern output from the candidate pattern selection unit 14 has converged, the candidate pattern as a convergence pattern, instructs the initial condition generation unit 11 to change one or both of the initial pattern and the seed value as the initial conditions, instructs the pattern generation unit 12 to perform initialization and newly start pattern generation, and instructs the candidate pattern selection unit 14 to initialize the candidate pattern.

[0085] In the transmission pattern determination unit 10B, the transmission pattern selection unit 16 selects, as a transmission pattern, one of the convergence patterns selected by the convergence determination unit 15, which has the highest evaluation value, and the end determination unit 17 selects, when the evaluation count for the generated patterns reaches the upper limit of the evaluation count, the transmission pattern selected by the transmission pattern selection unit 16 as an optimum transmission pattern.

[0086] Then, at the start of pattern generation, the pattern generation unit 12 selects, as a change target transmission point, one of the transmission points of the initial pattern output from the initial condition generation unit 11, based on the random number generated from the seed value, generates a pattern by changing a user equipment as a transmission destination only for the change target transmission point, and outputs the generated pattern. Every time the convergence determination unit 15 outputs the convergence pattern, the pattern generation unit 12 selects one of the transmission points of the convergence pattern as a change target point, generates a pattern by changing a user equipment as a transmission destination only for the change target transmission point, and outputs the generated pattern. The convergence determination unit 15 calculates, for every two patterns output at a predetermined convergence determination interval among the candidate patterns sequentially output from the candidate pattern selection unit 14, the change amount between the evaluation values of the two patterns, and detects, when it is continuously determined by the convergence determination count that the obtained change amount is smaller than the convergence determination threshold, that the evaluation value has converged.

[0087] With this arrangement, selection of the convergence pattern is repeatedly executed a plurality of times by changing the initial pattern every time the convergence pattern in which evaluation value has converged to the extreme value is selected by the hill-climbing method, and one of the obtained convergence patterns, which has the highest evaluation value, is selected as an optimum transmission pattern. Therefore, as compared to a case in which an initially found extreme value is selected as a final convergence value and the subsequent search is ended, it is possible to select, as an optimum transmission pattern, a pattern having a higher convergence value.

[0088] In the scheduling process, the scheduling period is defined. Thus, there is the upper limit of the evaluation count, and it is necessary to appropriately distribute the evaluation count to searches. According to this embodiment, since a new search starts after convergence is detected, it is possible to avoid a situation in which the evaluation count is short in one search and a next search is executed before convergence. Furthermore, starting a new search after detecting a convergence is equivalent to distributing a necessary and sufficient evaluation count to each of the searches, thereby making it possible to efficiently use the limited evaluation count.

[0089] In this embodiment, the initial condition generation unit 11 may generate a seed value to be used to generate a pattern and output it when outputting the initial pattern first, and generate a seed value having a unique value and output it every time the convergence determination unit 15 detects a convergence. When selecting a change target transmission point, the pattern generation unit 12 may select the change target transmission point based on a random number generated from the seed value output from the initial condition generation unit 11.

[0090] In general, a random number generated by calculation processing is a pseudo random number formed from a random number sequence determined based on the seed value. As the seed value changes, the random number sequence also changes. Thus, random numbers are generated in a different order. Consequently, every time the evaluation converges, a unique seed value is generated, and change target points are sequentially selected in a different order. When starting a new search by the hill-climbing method every time the evaluation value converges, convergence patterns can be searched by a route completely different from the last route, thereby implementing an efficient search with an unbiased search range.

[Second Embodiment]



[0091] A scheduling apparatus 10 according to the second embodiment of the present invention will be described next with reference to Fig. 6.

[0092] This arrangement is different from the first embodiment in that TP-specific transmission destination UE lists are input to an initial condition generation unit 11, and an initial pattern output from the initial condition generation unit 11 after detection of a convergence is different from that according to the first embodiment.

[0093] That is, in this embodiment, the initial condition generation unit 11 has a function of, when outputting an initial pattern first, generating and outputting an initial pattern in which a transmission stop is set for all transmission points, and every time a convergence determination unit 15 detects a convergence, selecting, for a selected one of the transmission points, one of user equipments which can set the transmission point as a transmission source as a new transmission destination, generating an initial pattern in which transmission stop is set for the transmission points except for that transmission point, and outputting it.

[0094] Referring to Fig. 6, the TP-specific transmission destination UE lists input from the outside of the scheduling apparatus 10 are also input to the initial condition generation unit 11 in addition to a pattern generation unit 12. The remaining components are the same as those in the first embodiment.

[0095] When the convergence determination unit 15 outputs a convergence flag = 1 indicating detection of a convergence of an evaluation value, the initial condition generation unit 11 executes initial condition generation processing shown in Fig. 7.

[0096] Referring to Fig. 7, the initial condition generation unit 11 randomly selects one TP as S_TP' (step 200), and randomly selects a new transmission destination UE from the transmission destination UE candidate list of S_TP' (step 201).

[0097] The initial condition generation unit 11 generates an initial pattern in which only the transmission destination of S_TP' is set as the selected new transmission destination UE, and all the transmission destinations of the TPs other than S_TP' are set to indicate a transmission stop (Blank) (step 202) .

[0098] Subsequently, after generating a seed value including a unique value different from the seed value that has been used so far (step 203), the initial condition generation unit 11 outputs the new initial pattern and seed value to the pattern generation unit 12 (step 204), thereby ending the series of initial condition generation processes.

[0099] Note that in Fig. 7, the reason why the seed value is changed after the initial pattern is updated is that S_TP selected first after initialization in the pattern generation unit 12 is made different from S_TP'. This can increase the possibility that a different convergence value is obtained. In an initial pattern generated immediately after the start instruction, the transmission destinations of all the TPs are set to "Blank".

[0100] In this embodiment, when generating an initial pattern after detecting a convergence, the transmission destination UE of S_TP' is randomly set. However, a transmission destination UE in a pattern (under the condition that "Blank" is set for TPs other than S_TP') having the second highest evaluation value may be set.

[Effect of Second Embodiment]



[0101] In this embodiment, when outputting an initial pattern first, the initial condition generation unit 11 generates and outputs the initial pattern in which a transmission stop is set for all transmission points. Every time the convergence determination unit 15 detects convergence, the initial condition generation unit 11 selects, for a selected one of the transmission points, as a new transmission destination, one of user equipments which can set the transmission point as a transmission source, generates an initial pattern in which a transmission stop is set for the transmission points except for that transmission point, and outputs it.

[0102] With this arrangement, every time the evaluation value converges, a different transmission point is set as an initial transmission point whose transmission destination is to be changed. Therefore, when starting a new search by the hill-climbing method every time the evaluation value converges, convergence patterns can be searched from a start point different from the last start point, thereby implementing an efficient search with an unbiased search range.

[Third Embodiment]



[0103] A scheduling apparatus 10 according to the third embodiment of the present invention will be described next with reference to Fig. 8.

[0104] As shown in Fig. 8, this arrangement is basically the same as in the first embodiment except for convergence determination conditions input to a convergence determination unit 15 and the operation of the convergence determination unit 15.

[0105] That is, the convergence determination unit 15 according to this embodiment has a table for recording the evaluation value of a candidate pattern sequentially output from a candidate pattern selection unit 14, and has a function of recording the evaluation value in the table at an interval of a convergence determination count before detecting an initial convergence and, once a convergence is detected, comparing, every time the candidate pattern selection unit 14 newly outputs the evaluation value, the output evaluation value with the evaluation value recorded in the table, and determining, when it is continuously determined by a predetermined match determination count that the evaluation values match, that the evaluation value has converged.

[0106] Note that in this embodiment, only the first result is stored in the table, and the evaluation value is only compared with the first result. The result to be compared with is not necessarily the first result. For example, a plurality of tables may be provided, results other than the first result may be stored, and the evaluation value may be compared with all the stored evaluation values. In this case, when it is continuously determined by the predetermined match determination count that one or more evaluation values match, it is determined that the evaluation value has converged.

[0107] In this embodiment, when no convergence value is obtained in the search when storing the second or subsequent result, the table may be overwritten at the time of the next search. For example, when no convergence value is obtained in the second search, the second result stored halfway is overwritten with the third result. When comparison with only the first result is performed, the same convergence pattern may be obtained in the second or subsequent search. To the contrary, by comparing the evaluation value with a result in which a convergent value is obtained in the second or subsequent search, the effect of readily obtaining a different result of a convergence pattern is produced.

[0108] In this embodiment, two tables each for storing a result may be provided. One of the tables stores the first result, as similar to the above-described embodiment, and the other stores the second result. When a convergence pattern is obtained in the second search, the evaluation value of the convergence pattern is compared with that of the convergence pattern obtained in the first search, and the table storing the convergence pattern having the lower evaluation value is overwritten with the third result. With this arrangement, even if the evaluation value of the convergence pattern obtained in the first search is low, comparison with a convergence pattern having a high evaluation value obtained in the second or subsequent search is possible and comparison of a convergence tendency at the time of a search can be performed only once. Furthermore, only two tables each for holding a result need to be provided, thereby obtaining the effect of reducing the scale.

[0109] With this arrangement, transition of the evaluation value in the first search is recorded in the table. When the evaluation value transits in the same way in the second or subsequent search, it is determined that the same convergence pattern is obtained, and thus a convergence is determined even before the convergence, and re-execution starts.

[0110] In transition of the pattern evaluation value shown in Fig. 9, (a) of Fig. 9 is a graph showing transition of the pattern evaluation value, and (b) of Fig. 9 shows an example of recording of the evaluation value in the table.

[0111] A convergence process shown in Figs. 10 and 11 has a feature in which a current internally held evaluation value is recorded in the table at a convergence determination interval in addition to the processing shown in Fig. 4 described above, and in the second or subsequent search, the evaluation value recorded in the table is compared with the current internally held evaluation value at the convergence determination interval, and when the number of times it is determined that the evaluation value recorded in the table matches the current internally held evaluation value reaches a match determination count input from the outside of the scheduling apparatus 10, the convergence is forcibly determined.

[0112] For this purpose, the convergence determiner 15 newly includes a table update inhibition flag (update is inhibited in the second or subsequent search) for identifying whether the search is the first one, and a table write index/table read index indicating a write/read position in the table. A match counter for measuring the number of times it is continuously determined that values match is newly included in the table.

[0113] Referring to Fig. 10, upon receiving a start instruction from the outside of the scheduling apparatus 10, the convergence determination unit 15 sets the table update inhibition flag to 0 (update permission) while setting the table write index to 1 (step 300), and then initializes the processing (step 301). In this initialization processing, the convergence flag is cleared to 0, and the current internally held evaluation value, a last internally held evaluation value, a convergence determination count value, and a convergence determination interval count value are respectively set to 0. In addition, the table read index and the match count value are respectively set to 0.

[0114] Subsequently, the convergence determination unit 15 receives a candidate pattern and an evaluation value from the candidate pattern selection unit 14 (step 302), and sets the received candidate pattern and evaluation value as an internally held pattern and the current internally held evaluation value (step 303).

[0115] The convergence determination unit 15 increments (+1) the convergence determination interval count value (step 304), and compares the convergence determination interval count value with a convergence determination interval (step 305). When the convergence determination interval count value is smaller than the convergence determination interval (NO in step 305), the process advances to step 310 (to be described later) of Fig. 10.

[0116] At this time, when the convergence determination interval count value is equal to or larger than the convergence determination interval (YES in step 305), the convergence determination unit 15 clears the convergence determination interval count value to 0 (step 306), and confirms the table update inhibition flag (step 307).

[0117] When the table update inhibition flag = 1 and update of the table is inhibited (NO in step 307), the process advances to step 320 of Fig. 11.

[0118] On the other hand, when the table update inhibition flag = 0 and update of the table is permitted (YES in step 307), the convergence determination unit 15 writes the current internally held evaluation value in the memory area of the table write index of the table (step 308), and increments the table write index (step 309), thereby advancing to step 323 of Fig. 11.

[0119] In step 320 of Fig. 11, the convergence determination unit 15 reads out the evaluation value from the memory area of the table read index of the table (step 320), and compares the readout evaluation value with the current internally held evaluation value (step 321).

[0120] When the readout evaluation value is equal to or larger than the current internally held evaluation value (NO in step 321), the convergence determination unit 15 clears the match count value to 0 (step 322), and advances to step 323 (to be described later).

[0121] On the other hand, when the readout evaluation value is smaller than the current internally held evaluation value (YES in step 321), the convergence determination unit 15 increments the match count value (step 324), and compares the match count value with a match determination count (step 325).

[0122] When the match count value has not reached the match determination count (NO in step 325), the process advances to step 323 (to be described later); otherwise (YES in step 325), the process advances to step 330.

[0123] In step 323, the convergence determination unit 15 calculates a difference evaluation value by subtracting the last internally held evaluation value from the current internally held evaluation value (step 323), and compares the difference evaluation value with a convergence determination threshold (step 326).

[0124] When the difference evaluation value is larger than the convergence determination threshold (NO in step 326), a change in evaluation value is large and there is no convergence tendency, and thus the convergence determination unit 15 sets the convergence determination count value to 0 (step 327), thereby advancing to step 310 of Fig. 10.

[0125] On the other hand, when the difference evaluation value is equal to or smaller than the convergence determination threshold (YES in step 326), a change in evaluation value is small and there is a convergence tendency, and thus the convergence determination unit 15 increments the convergence determination count value (step 328), and compares the convergence determination count value with the convergence determination count (step 329).

[0126] When the convergence determination count value has not reached the convergence determination count (NO in step 329), the process advances to step 310 of Fig. 10.

[0127] When the convergence determination count value has reached the convergence determination count (YES in step 329), the convergence determination unit 15 determines that the evaluation value has converged to a predetermined extreme value since the convergence tendency has continued for a predetermined count, and sets the convergence flag to 1 (step 330), and then sets the current internally held evaluation value as the last internally held evaluation value (step 331), thereby advancing to step 310 of Fig. 10.

[0128] In step 310 of Fig. 10, the convergence determination unit 15 outputs the convergence flag to an initial condition generation unit 11, a pattern generation unit 12, and the candidate pattern selection unit 14, outputs the convergence pattern including the current internally held evaluation value to the pattern generation unit 12 and a transmission pattern selection unit 16, and outputs the evaluation value to the transmission pattern selection unit 16 (step 310).

[0129] After that, the convergence determination unit 15 confirms the convergence flag (step 311). When the convergence flag = 1 and a convergence of the evaluation value has been detected (YES in step 311), the process returns to step 301 and a convergence determination for a new pattern generated based on different initial conditions starts to perform a new search by the hill-climbing method.

[0130] On the other hand, when the convergence flag = 0 and no convergence of the evaluation value has been detected (NO in step 311), the process returns to step 302 to start convergence determination for the candidate pattern and evaluation value newly output from the candidate pattern selection unit 14. This continues the search in progress by the hill-climbing method.

[0131] Note that in the above example, a match is determined when the current internally held evaluation value with the first result is equal to or smaller than the predetermined threshold. However, a match determination method is not limited to this. For example, when a difference from the first result is equal to or larger than the threshold but the evaluation value is lower than the first result, a match may be determined. That is, in step 321 of Fig. 11, although the determination condition is "=" in the above embodiment, "≥" is set as a determination condition in this case. This can reduce the possibility that a result worse than the first result is obtained.

[0132] Alternatively, the threshold may be gradually decreased. At the initial stage of a search, the threshold is set large. As the search progresses, the threshold may be decreased.

[0133] In an example, shown in Fig. 12, of the relationship between the evaluation value and the elapsed time, in the first search, a search is repeated until the evaluation value converges to obtain a convergence pattern. After the convergence pattern is obtained, the initial conditions are changed to perform re-execution. In the second search, a search is performed while comparing whether a convergence tendency is the same as that in the first search. In this case, since the convergence tendencies in the first and second searches are different, a search is repeated until a convergence value is obtained without interrupting the second search. Similarly, the third search starts. Since a convergence tendency in the third search is compared with that in the first search, and it is determined that the convergence tendencies are identical, the search is interrupted before a convergence value is obtained, and the initial conditions are changed to perform re-execution.

[Effect of Third Embodiment]



[0134] As described above, the convergence determination unit 15 includes a table for recording the evaluation value of a candidate pattern sequentially output from the candidate pattern selection unit 14. Before detecting an initial convergence, the convergence determination unit 15 records the evaluation value in the table at an interval of a convergence determination count. Once a convergence is detected, every time the candidate pattern selection unit 14 newly outputs the evaluation value, the convergence determination unit 15 compares the output evaluation value with the evaluation value recorded in the table, and determines, when it is continuously determined by the predetermined match determination count that the evaluation values match, that the evaluation value has converged.

[0135] With this arrangement, transition of the evaluation value in the first search is recorded in the table. When the evaluation value transits in the same way in the second or subsequent search, it is determined that the same convergence pattern is obtained, and thus a convergence is determined even before the convergence, and re-execution starts. Therefore, before the evaluation value actually converges, the convergence can be forcibly determined, thereby making it possible to shorten the time required for the scheduling processing.

[Fourth Embodiment]



[0136] The arrangement of a scheduling apparatus 10 according to the fourth embodiment of the present invention will be described next with reference to Fig. 13.

[0137] This arrangement is obtained by arranging, in parallel, pattern generation units 12, pattern evaluation units 13, candidate pattern selection units 14, and convergence determination units 15 according to the first embodiment, as shown in Fig. 13. This arrangement can select a transmission pattern from more convergence patterns.

[0138] That is, the scheduling apparatus 10 according to this embodiment includes a plurality of processing systems each including the pattern generation unit 12, pattern evaluation unit 13, candidate pattern selection unit 14, and convergence determination unit 15.

[0139] An initial condition generation unit 11 has a function of generating and outputting different seed values for the respective processing systems.

[0140] A transmission pattern selection unit 16 has a function of selecting, as a transmission pattern, one of convergence patterns output from the processing systems, which has the highest evaluation value.

[0141] In the example of the arrangement shown in Fig. 13, processing system A including a pattern generation unit 12A, a pattern evaluation unit 13A, a candidate pattern selection unit 14A, and a convergence determination unit 15A, and processing system B including a pattern generation unit 12B, a pattern evaluation unit 13B, a candidate pattern selection unit 14B, and a convergence determination unit 15B are provided. The initial condition generation unit 11, the transmission pattern selection unit 16, and an end determination unit 17 are common to processing systems A and B. The number of processing systems arranged in parallel is two but the present invention is not limited to this.

[0142] In this arrangement, upon receiving a start instruction from the outside of the scheduling apparatus 10, the initial condition generation unit 11 outputs an initial pattern and seed values A and B to the pattern generation units 12A and 12B. Seed values A and B respectively output to the pattern generation units 12A and 12B are different from each other, but the same initial pattern is sequentially output to the pattern generation units 12A and 12B.

[0143] When the convergence determination unit 15A outputs convergence flag A, the initial condition generation unit 11 generates seed value A that is unique in all the processing systems, and outputs it to the pattern generation unit 12A together with the initial pattern. Similarly, when the convergence determination unit 15B outputs convergence flag B, the initial condition generation unit 11 generates seed value B that is unique in all the processing systems, and outputs it to the pattern generation unit 12B together with the initial pattern. This can execute parallel processes under different initial conditions.

[0144] The transmission pattern selection unit 16 is the same as that according to the first embodiment, and selects, as a transmission pattern, one of the convergence patterns output from the convergence determination units 15A and 15B, which has the highest evaluation value, and outputs the selected pattern to the end determination unit 17.

[Effect of Fourth Embodiment]



[0145] As described above, in this embodiment, the plurality of processing systems each including the pattern generation unit 12, the pattern evaluation unit 13, the candidate pattern selection unit 14, and the convergence determination unit 15 are provided. The initial condition generation unit 11 generates and outputs different seed values for the respective processing systems. The transmission pattern selection unit 16 selects, as a transmission pattern, one of the convergence patterns output form the processing systems, which has the highest evaluation value.

[0146] This parallelly executes searches based on the different seed values for the respective processing systems. Therefore, it is possible to select a transmission pattern from more convergence patterns within a short processing time.

[Extension of Embodiments]



[0147] The present invention has been described above with reference to the embodiments, but is not limited to these embodiments. Various changes understandable by those skilled in the art can be made for the arrangements and details of the present invention without departing from the scope of the invention, as defined by the appended set of claims. In addition, the embodiments can be arbitrarily combined and implemented within a consistent range.

Explanation of the Reference Numerals and Signs



[0148] 10...scheduling apparatus, 10A...convergence pattern selection unit, 10B...transmission pattern determination unit, 11...initial condition generation unit, 12, 12A, 12B...pattern generation unit, 13, 13A, 13B...pattern evaluation unit, 14, 14A, 14B...candidate pattern selection unit, 15, 15A, 15B...convergence determination unit, 16...transmission pattern selection unit, 17...end determination unit


Claims

1. A scheduling apparatus (10) configured to generate a plurality of patterns each indicating combinations of a plurality of transmission points forming a radio network system and user equipments for performing radio communication with the transmission points, and to select by a predetermined algorithm for solving a combinatorial optimization problem an optimum transmission pattern indicating optimum combinations of transmission points and user equipments based on evaluation values of the patterns, the apparatus comprising:

a convergence pattern selection unit (10A) configured to generate sequentially a plurality of different patterns based on designated initial conditions, to select, as a convergence pattern, the pattern in which evaluation value has converged to an extreme value, and repeatedly execute selection of the convergence pattern by changing the initial conditions every time the convergence pattern is selected; and

a transmission pattern determination unit (10B) configured to select, as the optimum transmission pattern, one of the convergence patterns with the highest evaluation value obtained by the convergence pattern selection unit (10A),

wherein the convergence pattern selection unit (10A) includes:

an initial condition generation unit (11) configured to generate and output an initial pattern and a seed value being a unique value for generating a random number as the initial conditions;

a pattern generation unit (12, 12A, 12B) configured to sequentially generate the pattern by sequentially changing some of the combinations of the initial pattern output from the initial condition generation unit (11) based on the random number generated from the seed value;

a pattern evaluation unit (13, 13A, 13B) configured to sequentially calculate the evaluation value of the pattern every time the pattern generation unit (12, 12A, 12B) outputs the pattern, the evaluation value being a value obtained by dividing an instantaneous throughput of a transmission destination user equipment corresponding to the transmission point by an average throughput based on a proportional-fairness method;

a candidate pattern selection unit (14, 14A, 14B) including an internally held pattern and an internally held evaluation value and configured to compare, every time the pattern generation unit (12, 12A, 12B) outputs the pattern, the evaluation value output from the pattern evaluation unit (13, 13A, 13B) with the internally held evaluation value, outputs the internally held pattern as a candidate pattern when the evaluation value output from the pattern evaluation unit (13, 13A, 13B) is equal to or smaller than the internally held evaluation value, and sets the pattern output from the pattern generation unit (12, 12A, 12B) and the evaluation value output from the pattern evaluation unit (13, 13A, 13B) as the internally held pattern and the internally held evaluation value, respectively, when the evaluation value output from the pattern evaluation unit (13, 13A, 13B) is larger than the internally held evaluation value, and

a convergence determination unit (15, 15A, 15B) configured to calculate a change amount between the evaluation values of the two patterns for every two patterns output at a predetermined convergence determination interval among the candidate patterns sequentially output from the candidate pattern selection unit (14, 14A, 14B), to determine that the evaluation values are converged to the extreme value when the obtained change amount is continuously determined to be smaller than a convergence determination threshold a number of times equal to a convergence determination count, to select the candidate pattern as the convergence pattern when detecting that the evaluation value has converged, to instruct the initial condition generation unit (11) to change one or both of the initial pattern and the seed value, which are initial conditions, to instruct the pattern generation unit (12, 12A, 12B) to perform initialization and newly start pattern generation, and to instruct the candidate pattern selection unit (14, 14A, 14B) to initialize the internally held pattern, wherein initialization of the internally held pattern being deletion of the transmission destinations of all the transmission points to indicate transmission stop.


 
2. The scheduling apparatus (10) according to claim 1, wherein
the initial condition generation unit (11) is configured to generate and output, when outputting the initial pattern for the first time, a seed value to be used when generating the pattern, and to generate and output a seed value having a unique value every time the convergence determination unit (15, 15A, 15B) detects the convergence, and
the pattern generation unit (12, 12A, 12B) is configured to select, when generating a pattern, a transmission point to be changed based on a random number generated from the seed value output from the initial condition generation unit (11).
 
3. The scheduling apparatus (10) according to claim 1 or 2, wherein
the initial condition generation unit (11) is configured to generate and output, when outputting the initial pattern for the first time, an initial pattern in which a transmission stop is set for all the transmission points, and every time the convergence determination unit (15, 15A, 15B) detects the convergence, the initial condition generation unit (11) is configured to select, as a new transmission destination, one of user equipments each of which can have one transmission point selected from among the transmission points as a transmission source, and to generate and output an initial pattern in which a transmission stop is set for the transmission points other than the transmission point.
 
4. The scheduling apparatus (10) according to any one of claims 1 to 3, wherein
the convergence determination unit (15, 15A, 15B) is configured to include at least a table for recording the evaluation value of the candidate pattern sequentially output from the candidate pattern selection unit (14, 14A, 14B), to record the evaluation value in the table at intervals corresponding to the convergence determination count, and after the convergence is detected for the first time, every time the candidate pattern selection unit (14, 14A, 14B) newly outputs the evaluation value, the convergence determination unit is configured to compare the output evaluation value with at least one evaluation value recorded in the table, and to determine that the evaluation value has converged when the evaluation value is continuously determined to be equal to or smaller than the recorded evaluation value a number of times equal to a predetermined match determination count.
 
5. The scheduling apparatus (10) according to claim 2, comprising:

a plurality of processing systems each including the pattern generation unit (12A, 12B), the pattern evaluation unit (13A, 13B), the candidate pattern selection unit (14A, 14B), and the convergence determination unit (15A, 15B),

wherein the initial condition generation unit (11) generates and outputs the seed value that is different for each of the processing systems, and

the transmission pattern determination unit (10B) selects, as the transmission pattern, one of the convergence patterns with the highest evaluation value output from the processing systems.


 
6. A scheduling method of generating a plurality of patterns each indicating combinations of a plurality of transmission points forming a radio network system and user equipments for performing radio communication with the transmission points, and selecting, by a predetermined algorithm for solving a combinatorial optimization problem, an optimum transmission pattern indicating optimum combinations of transmission points and user equipments based on evaluation values of the patterns the method comprising:

a convergence pattern selection step of sequentially generating a plurality of different patterns based on designated initial conditions, selecting, as a convergence pattern, the pattern in which evaluation value has converged to an extreme value, and repeatedly executing selection of the convergence pattern by changing the initial conditions every time the convergence pattern is selected; and

a transmission pattern determination step of selecting, as the optimum transmission pattern, one of the convergence patterns with the highest evaluation value obtained in the convergence pattern selection step,

wherein the convergence pattern selection step includes:

an initial condition generation step of generating and outputting an initial pattern and a seed value being a unique value for generating a random number as the initial conditions;

a pattern generation step of sequentially generating the pattern by sequentially changing some of the combinations of the initial pattern output in the initial condition generation step based on the random number generated from the seed value;

a pattern evaluation step of sequentially calculating the evaluation value of the pattern every time the pattern is output in the pattern generation step, the evaluation value being a value obtained by dividing an instantaneous throughput of a transmission destination user equipment corresponding to the transmission point by an average throughput based on a proportional-fairness method;

a candidate pattern selection step of comparing, every time the pattern is output in the pattern generation step, the evaluation value output in the pattern evaluation step with an internally held evaluation value, outputting an internally held pattern as a candidate pattern when the evaluation value output in the pattern evaluation step is equal to or smaller than the internally held evaluation value, and setting the pattern output in the pattern generation step and the evaluation value output in the pattern evaluation step as the internally held pattern and the internally held evaluation value, respectively, when the evaluation value output in the pattern evaluation step is larger than the internally held evaluation value, and

a convergence determination step of calculating a change amount between the evaluation values of the two patterns for every two patterns output at a predetermined convergence determination interval among the candidate patterns sequentially output in the candidate pattern selection step, determining that the evaluation values are converged to the extreme value when the obtained change amount is continuously determined to be smaller than a convergence determination threshold a number of times equal to a convergence determination count, selecting the candidate pattern as the convergence pattern when detecting that the evaluation value has converged, instructing to change one or both of the initial pattern and the seed value, which are initial conditions, instructing to perform initialization and newly start pattern generation, and instructing to initialize the internally held pattern, wherein initialization of the internally held pattern being deletion of the transmission destinations of all the transmission points to indicate transmission stop.


 


Ansprüche

1. Eine Scheduling-Vorrichtung (10), die so konfiguriert ist, dass sie eine Mehrzahl von Mustern erzeugt, die jeweils Kombinationen von einer Mehrzahl von Übertragungspunkten, die ein Funknetzwerksystem bilden, und Benutzergeräten zur Durchführung von Funkkommunikation mit den Übertragungspunkten angeben, und dass sie durch einen vorbestimmten Algorithmus zur Lösung eines kombinatorischen Optimierungsproblems ein optimales Übertragungsmuster auswählt, das optimale Kombinationen von Übertragungspunkten und Benutzergeräten auf der Grundlage von Beurteilungswerten der Muster angibt, wobei die Vorrichtung umfasst:

eine Konvergenzmuster-Auswahleinheit (10A), die so konfiguriert ist, dass sie sequentiell eine Mehrzahl von verschiedenen Mustern auf der Grundlage von bestimmten Anfangsbedingungen erzeugt, dass sie als Konvergenzmuster das Muster auswählt, in dem der Beurteilungswert zu einem Extremwert konvergiert hat, und dass sie die Auswahl des Konvergenzmusters wiederholt ausführt, indem die Anfangsbedingungen jedes Mal geändert werden, wenn das Konvergenzmuster ausgewählt wird; und

eine Übertragungsmuster-Bestimmungseinheit (10B), die so konfiguriert ist, dass sie als das optimale Übertragungsmuster eines der Konvergenzmuster mit dem höchsten von der Konvergenzmuster-Auswahleinheit (10A) erhaltenen Beurteilungswert auswählt,

wobei die Konvergenzmuster-Auswahleinheit (10A) aufweist:

eine Anfangszustands-Erzeugungseinheit (11), die so konfiguriert ist, dass sie ein Anfangsmuster und einen Startwert, der ein eindeutiger Wert zum Erzeugen einer Zufallszahl ist, als Anfangsbedingungen erzeugt und ausgibt;

eine Mustererzeugungseinheit (12, 12A, 12B), die so konfiguriert ist, dass sie das Muster sequentiell erzeugt, indem sie einige der Kombinationen der Ausgangsmuster, die von der Ausgangszustands-Erzeugungseinheit (11) ausgegeben werden, auf der Grundlage der aus dem Startwert erzeugten Zufallszahl sequentiell ändert;

eine Musterbeurteilungseinheit (13, 13A, 13B), die so konfiguriert ist, dass sie den Beurteilungswert des Musters jedes Mal sequentiell berechnet, wenn die Mustererzeugungseinheit (12, 12A, 12B) das Muster ausgibt, wobei der Beurteilungswert ein Wert ist, der durch Dividieren eines momentanen Durchsatzes eines Übertragungsziel-Benutzergeräts, das dem Übertragungspunkt entspricht, durch einen durchschnittlichen Durchsatz auf der Grundlage eines Proportional-Gerechtigkeits-Verfahrens erhalten wird;

eine Kandidatenmuster-Auswahleinheit (14, 14A, 14B), die ein intern gehaltenes Muster und einen intern gehaltenen Beurteilungswert aufweist und so konfiguriert ist, dass sie jedes Mal, wenn die Mustererzeugungseinheit (12, 12A, 12B) das Muster ausgibt, den von der Musterbeurteilungseinheit (13, 13A, 13B) ausgegebenen Beurteilungswert mit dem intern gehaltenen Beurteilungswert vergleicht, das intern gehaltene Muster als Kandidatenmuster ausgibt, wenn der von der Musterbeurteilungseinheit (13, 13A, 13B) ausgegebene Beurteilungswert gleich oder kleiner als der intern gehaltene Beurteilungswert ist und das von der Mustererzeugungseinheit (12, 12A, 12B) ausgegebene Muster und den von der Musterbeurteilungseinheit (13, 13A, 13B) ausgegebenen Beurteilungswert als das intern gehaltene Muster bzw. den intern gehaltenen Beurteilungswert festlegt, wenn der von der Musterbeurteilungseinheit (13, 13A, 13B) ausgegebene Beurteilungswert größer als der intern gehaltene Beurteilungswert ist, und

eine Konvergenzbestimmungseinheit (15, 15A, 15B), die so konfiguriert ist, dass sie eine Änderungsbetrag zwischen den Beurteilungswerten der beiden Muster für jede zwei Muster berechnet, die in einem vorbestimmten Konvergenzbestimmungsintervall unter den Kandidatenmustern ausgegeben werden, die von der Kandidatenmuster-Auswahleinheit (14, 14A, 14B) sequentiell ausgegeben werden, dass sie bestimmt, dass die Beurteilungswerte auf den Extremwert konvergiert sind, wenn die erhaltene Änderungsgröße kontinuierlich so bestimmt wird, dass sie eine Anzahl von Malen, die gleich einer Konvergenzbestimmungszahl ist, kleiner als ein Konvergenzbestimmungsschwellenwert ist, dass sie das Kandidatenmuster als das Konvergenzmuster auswählt, wenn festgestellt wird, dass der Beurteilungswert konvergiert hat, um die Anfangsbedingungs-Erzeugungseinheit (11) anzuweisen, eines oder beide des Anfangsmusters und des Startwertes, die Anfangsbedingungen sind, zu ändern, dass sie die Mustererzeugungseinheit (12, 12A, 12B) anweist, eine Initialisierung durchzuführen und eine Mustererzeugung neu zu starten, und dass sie die Kandidatenmuster-Auswahleinheit (14, 14A, 14B) anweist, das intern gehaltene Muster zu initialisieren, wobei die Initialisierung des intern gehaltenen Musters das Löschen der Übertragungsziele aller Übertragungspunkte ist, um den Übertragungsstopp anzuzeigen.


 
2. Scheduling-Vorrichtung (10) nach Anspruch 1, wobei

die Anfangszustands-Erzeugungseinheit (11) so konfiguriert ist, dass sie bei der ersten Ausgabe des Anfangsmusters einen bei der Erzeugung des Musters zu verwendenden Startwert erzeugt und ausgibt, und dass sie jedes Mal, wenn die Konvergenz-Bestimmungseinheit (15, 15A, 15B) die Konvergenz erfasst, einen Startwert mit einem eindeutigen Wert erzeugt und ausgibt, und

die Mustererzeugungseinheit (12, 12A, 12B) so konfiguriert ist, dass sie bei der Erzeugung eines Musters einen zu ändernden Übertragungspunkt auf der Grundlage einer Zufallszahl auswählt, die aus dem von der Anfangszustands-Erzeugungseinheit (11) ausgegebenen Startwert erzeugt wird.


 
3. Scheduling-Vorrichtung (10) nach Anspruch 1 oder 2, wobei
die Anfangszustands-Erzeugungseinheit (11) so konfiguriert ist, dass sie, wenn sie das Anfangsmuster zum ersten Mal ausgibt, ein Anfangsmuster erzeugt und ausgibt, in dem ein Übertragungsstopp für alle Übertragungspunkte eingestellt ist, und jedes Mal, wenn die Konvergenzbestimmungseinheit (15, 15A, 15B) die Konvergenz erfasst, die Anfangszustands-Erzeugungseinheit (11) so konfiguriert ist, dass sie als neues Übertragungsziel eines von Benutzergeräten auswählt, von denen jedes einen Übertragungspunkt haben kann, der aus den Übertragungspunkten als Übertragungsquelle ausgewählt wird, und dass sie ein Anfangsmuster erzeugt und ausgibt, in dem ein Übertragungsstopp für die anderen Übertragungspunkte als den Übertragungspunkt eingestellt ist.
 
4. Scheduling-Vorrichtung (10) nach einem der Ansprüche 1 bis 3, wobei die Konvergenzbestimmungseinheit (15, 15A, 15B) so konfiguriert ist, dass sie mindestens eine Tabelle zum Aufzeichnen des Beurteilungswertes des Kandidatenmusters aufweist, der sequentiell von der Kandidatenmuster-Auswahleinheit (14, 14A, 14B) ausgegeben wird, dass sie den Beurteilungswert in der Tabelle in Intervallen aufzeichnet, die der Konvergenzbestimmungszahl entsprechen, und nachdem die Konvergenz zum ersten Mal erkannt wurde, jedes Mal, wenn die Kandidatenmuster-Auswahleinheit (14, 14A, 14B) den Beurteilungswert neu ausgibt, ist die Konvergenzbestimmungseinheit so konfiguriert, dass sie den ausgegebenen Beurteilungswert mit mindestens einem in der Tabelle aufgezeichneten Beurteilungswert vergleicht und dass sie feststellt, dass der Beurteilungswert konvergiert hat, wenn der Beurteilungswert fortlaufend so bestimmt wird, dass er mehrere Male gleich einer vorbestimmten Übereinstimmungsbestimmungszahl gleich oder kleiner als der aufgezeichnete Beurteilungswert ist.
 
5. Scheduling-Vorrichtung (10) nach Anspruch 2, bestehend aus:

eine Mehrzahl von Verarbeitungssystemen, die jeweils die Mustererzeugungseinheit (12A, 12B), die Musterbeurteilungseinheit (13A, 13B), die Kandidatenmusterauswahleinheit (14A, 14B) und die Konvergenzbestimmungseinheit (15A, 15B) aufweisen,

wobei die Anfangszustands-Erzeugungseinheit (11) den Startwert erzeugt und ausgibt, der für jedes der Verarbeitungssysteme unterschiedlich ist, und die Übertragungsmusters-Bestimmungseinheit (10B) als Übertragungsmuster eines der Konvergenzmuster mit dem höchsten von den Verarbeitungssystemen ausgegebenen Beurteilungswert auswählt.


 
6. Scheduling-Verfahren zum Erzeugen einer Mehrzahl von Mustern, die jeweils Kombinationen einer Mehrzahl von ein Funknetzwerksystem bildenden Übertragungspunkten und Benutzergeräten zur Durchführung von Funkkommunikation mit den Übertragungspunkten angeben, und zum Auswählen eines optimalen Übertragungsmusters, das optimale Kombinationen von Übertragungspunkten und Benutzergeräten basierend auf Beurteilungswerten der Muster angibt, durch einen vorbestimmten Algorithmus zum Lösen eines kombinatorischen Optimierungsproblems, wobei das Verfahren umfasst:

einen Konvergenzmuster-Auswahlschritt des sequentiellen Erzeugens einer Mehrzahl von verschiedenen Mustern auf der Grundlage von bestimmten Anfangsbedingungen, des Auswählens des Musters, in dem der Beurteilungswert zu einem Extremwert konvergiert hat, als Konvergenzmuster und des wiederholten Ausführens der Auswahl des Konvergenzmusters durch Ändern der Anfangsbedingungen bei jeder Auswahl des Konvergenzmusters; und

einen Übertragungsmuster-Bestimmungsschritt zum Auswählen als optimales Übertragungsmuster eines der Konvergenzmuster mit dem höchsten im Konvergenzmuster-Auswahlschritt erhaltenen Beurteilungswert,

wobei der Konvergenzmusterauswahlschritt aufweist:

einen Anfangsbedingungs-Erzeugungsschritt zur Erzeugung und Ausgabe eines Anfangsmusters und eines Startwertes, der ein eindeutiger Wert zur Erzeugung einer Zufallszahl ist, als Anfangsbedingungen;

einen Mustererzeugungsschritt zur sequentiellen Erzeugung des Musters durch sequentielles Ändern einiger der Kombinationen der im Anfangsbedingungs-erzeugungsschritt ausgegebenen Anfangsmuster auf der Grundlage der aus dem Startwert erzeugten Zufallszahl;

einen Musterbeurteilungsschritt zum sequentiellen Berechnen des Beurteilungswertes des Musters jedes Mal, wenn das Muster im Mustererzeugungsschritt ausgegeben wird, wobei der Beurteilungswert ein Wert ist, der durch Dividieren eines momentanen Durchsatzes eines Übertragungszielbenutzergerätes, das dem Übertragungspunkt entspricht, durch einen durchschnittlichen Durchsatz auf der Grundlage eines Proportional-Gerechtigkeits-Verfahrens erhalten wird;

einen Kandidatenmuster-Auswahlschritt zum Vergleichen jedes Mal, wenn das Muster im Mustererzeugungsschritt ausgegeben wird, der im Musterbeurteilungsschritt ausgegebene Beurteilungswert mit einem intern gehaltenen Beurteilungswert, zum Ausgeben eines intern gehaltenen Musters als Kandidatenmuster, wenn der im Musterbeurteilungsschritt ausgegebene Beurteilungswert gleich oder kleiner als der intern gehaltene Beurteilungswert ist, und zum Einstellen des im Mustererzeugungsschritt ausgegebenen Musters und des im Musterbeurteilungsschritt ausgegebenen Beurteilungswerts als das intern gehaltene Muster bzw. der intern gehaltene Beurteilungswert, wenn der im Musterbeurteilungsschritt ausgegebene Beurteilungswert größer als der intern gehaltene Beurteilungswert ist, und

einen Konvergenzbestimmungsschritt zum Berechnen einer Änderungsgröße zwischen den Beurteilungswerten der beiden Muster für jede zwei Muster, die in einem vorbestimmten Konvergenzbestimmungsintervall unter den Kandidatenmustern ausgegeben werden, die in dem Kandidatenmuster-Auswahlschritt sequentiell ausgegeben werden, zum Bestimmen, dass die Beurteilungswerte zu dem Extremwert konvergieren, wenn die erhaltene Änderungsgröße kontinuierlich so bestimmt wird, dass sie eine Anzahl von Malen, die gleich einer Konvergenzbestimmungszahl ist, kleiner als ein Konvergenzbestimmungsschwellenwert ist, zum Auswählen des Kandidatenmusters als das Konvergenzmuster, wenn festgestellt wird, dass der Beurteilungswert konvergiert hat, zum Anweisen, eines oder beide des Anfangsmusters und des Startwertes, die Anfangsbedingungen sind, zu ändern, zum Anweisen, die Initialisierung und die Erzeugung eines Startmusters neu durchzuführen, und zum Anweisen, das intern gehaltene Muster zu initialisieren, wobei die Initialisierung des intern gehaltenen Musters das Löschen der Übertragungsziele aller Übertragungspunkte ist, um den Übertragungsstopp anzuzeigen.


 


Revendications

1. Appareil de planification (10) configuré pour générer une pluralité de motifs indiquant chacun des combinaisons d'une pluralité de points de transmission formant un système de réseau radio et d'équipements utilisateur pour effectuer une communication radio avec les points de transmission, et pour sélectionner par un algorithme prédéterminé pour résoudre un problème d'optimisation combinatoire un motif de transmission optimum indiquant des combinaisons optimums de points de transmission et d'équipements utilisateur sur la base de valeurs d'évaluation des motifs, l'appareil comprenant:

une unité de sélection de motif de convergence (10A) configurée pour générer séquentiellement une pluralité de motifs différents sur la base de conditions initiales désignées, pour sélectionner, comme un motif de convergence, le motif dans lequel une valeur d'évaluation a convergé vers une valeur extrême, et exécuter de manière répétée la sélection du motif de convergence en changeant les conditions initiales à chaque fois que le motif de convergence est sélectionné; et

une unité de détermination de motif de transmission (10B) configurée pour sélectionner, comme le motif de transmission optimum, l'un des motifs de convergence avec la valeur d'évaluation la plus élevée obtenue par l'unité de sélection de motif de convergence (10A),

dans lequel l'unité de sélection de motif de convergence (10A) inclut:

une unité de génération de condition initiale (11) configurée pour générer et sortir un motif initial et une valeur de graine étant une valeur unique pour générer un nombre aléatoire comme les conditions initiales;

une unité de génération de motif (12, 12A, 12B) configurée pour générer séquentiellement le motif en changeant séquentiellement certaines des combinaisons du motif initial sorti de l'unité de génération de condition initiale (11) sur la base du nombre aléatoire généré à partir de la valeur de graine;

une unité d'évaluation de motif (13, 13A, 13B) configurée pour calculer séquentiellement la valeur d'évaluation du motif chaque fois que l'unité de génération de motif (12, 12A, 12B) sort le motif, la valeur d'évaluation étant une valeur obtenue en divisant un débit instantané d'un équipement utilisateur de destination de transmission correspondant au point de transmission par un débit moyen sur la base d'une méthode d'équité proportionnelle;

une unité de sélection de motif candidat (14, 14A, 14B) incluant un motif conservé de façon interne et une valeur d'évaluation conservée de façon interne et configurée pour comparer, chaque fois que l'unité de génération de motif (12, 12A, 12B) sort le motif, la valeur d'évaluation sortie de l'unité d'évaluation de motif (13, 13A, 13B) avec la valeur d'évaluation conservée de façon interne, sort le motif conservé de façon interne comme un motif candidat lorsque la valeur d'évaluation sortie de l'unité d'évaluation de motif (13, 13A, 13B) est égale ou inférieure à la valeur d'évaluation conservée de façon interne, et définit le motif sorti de l'unité de génération de motif (12, 12A, 12B) et la valeur d'évaluation sortie de l'unité d'évaluation de motif (13, 13A, 13B) comme le motif conservé de façon interne et la valeur d'évaluation conservée de façon interne, respectivement, lorsque la valeur d'évaluation sortie de l'unité d'évaluation de motif (13, 13A, 13B) est supérieure à la valeur d'évaluation conservée de façon interne, et

une unité de détermination de convergence (15, 15A, 15B) configurée pour calculer une quantité de changement entre les valeurs d'évaluation des deux motifs tous les deux motifs sortis à un intervalle de détermination de convergence prédéterminé parmi les motifs candidats sortis séquentiellement de l'unité de sélection de motif candidat (14, 14A, 14B), pour déterminer que les valeurs d'évaluation sont convergées vers la valeur extrême lorsque la quantité de changement obtenue est déterminée de façon continue comme étant inférieure à un seuil de détermination de convergence un nombre de fois égal à un compte de détermination de convergence, pour sélectionner le motif candidat comme le motif de convergence lors de la détection que la valeur d'évaluation a convergé, pour donner pour instruction à l'unité de génération de condition initiale (11) de changer un ou les deux du motif initial et de la valeur de graine, qui sont des conditions initiales, pour donner pour instruction à l'unité de génération de motif (12, 12A, 12B) d'effectuer l'initialisation et de commencer à nouveau la génération de motif, et pour donner pour instruction à l'unité de sélection de motif candidat (14, 14A, 14B) d'initialiser le motif conservé de façon interne, dans lequel l'initialisation du motif conservé de façon interne étant la suppression des destinations de transmission de tous les points de transmission pour indiquer l'arrêt de transmission.


 
2. Appareil de planification (10) selon la revendication 1, dans lequel l'unité de génération de condition initiale (11) est configurée pour générer et sortir, lors de la sortie du motif initial pour la première fois, une valeur de graine à utiliser lors de la génération du motif, et pour générer et sortir une valeur de graine ayant une valeur unique chaque fois que l'unité de détermination de convergence (15, 15A, 15B) détecte la convergence, et
l'unité de génération de motif (12, 12A, 12B) est configurée pour sélectionner, lors de la génération d'un motif, un point de transmission à changer sur la base d'un nombre aléatoire généré à partir de la valeur de graine sortie de l'unité de génération de condition initiale (11).
 
3. Appareil de planification (10) selon la revendication 1 ou 2, dans lequel
l'unité de génération de condition initiale (11) est configurée pour générer et sortir, lors de la sortie du motif initial pour la première fois, un motif initial dans lequel un arrêt de transmission est défini pour tous les points de transmission, et chaque fois que l'unité de détermination de convergence (15, 15A, 15B) détecte la convergence, l'unité de génération de condition initiale (11) est configurée pour sélectionner, comme une nouvelle destination de transmission, l'un des équipements utilisateur dont chacun peut avoir un point de transmission sélectionné parmi les points de transmission comme une source de transmission, et pour générer et sortir un motif initial dans lequel un arrêt de transmission est défini pour les points de transmission autres que le point de transmission.
 
4. Appareil de planification (10) selon l'une quelconque des revendications 1 à 3, dans lequel
l'unité de détermination de convergence (15, 15A, 15B) est configurée pour inclure au moins une table pour enregistrer la valeur d'évaluation du motif candidat sorti séquentiellement de l'unité de sélection de motif candidat (14, 14A, 14B), pour enregistrer la valeur d'évaluation dans la table à des intervalles correspondant au compte de détermination de convergence, et après que la convergence est détectée pour la première fois, chaque fois que l'unité de sélection de motif candidat (14, 14A, 14B) sort à nouveau la valeur d'évaluation, l'unité de détermination de convergence est configurée pour comparer la valeur d'évaluation sortie avec au moins une valeur d'évaluation enregistrée dans la table, et pour déterminer que la valeur d'évaluation a convergé lorsque la valeur d'évaluation est déterminée de façon continue comme étant égale ou inférieure à la valeur d'évaluation enregistrée un nombre de fois égal à un compte de détermination de correspondance prédéterminé.
 
5. Appareil de planification (10) selon la revendication 2, comprenant:

une pluralité de systèmes de traitement incluant chacun l'unité de génération de motif (12A, 12B), l'unité d'évaluation de motif (13A, 13B), l'unité de sélection de motif candidat (14A, 14B), et l'unité de détermination de convergence (15A, 15B),

dans lequel l'unité de génération de condition initiale (11) génère et sort la valeur de graine qui est différente pour chacun des systèmes de traitement, et

l'unité de détermination de motif de transmission (10B) sélectionne, comme le motif de transmission, l'un des motifs de convergence avec la valeur d'évaluation la plus élevée sortie des systèmes de traitement.


 
6. Procédé de planification consistant à générer une pluralité de motifs indiquant chacun des combinaisons d'une pluralité de points de transmission formant un système de réseau radio et d'équipements utilisateur pour effectuer une communication radio avec les points de transmission, et sélectionner, par un algorithme prédéterminé pour résoudre un problème d'optimisation combinatoire, un motif de transmission optimum indiquant des combinaisons optimums de points de transmission et d'équipements utilisateur sur la base de valeurs d'évaluation des motifs, le procédé comprenant:

une étape de sélection de motif de convergence consistant à générer séquentiellement une pluralité de motifs différents sur la base de conditions initiales désignées, sélectionner, comme un motif de convergence, le motif dans lequel une valeur d'évaluation a convergé vers une valeur extrême, et exécuter de manière répétée la sélection du motif de convergence en changeant les conditions initiales à chaque fois que le motif de convergence est sélectionné ; et

une étape de détermination de motif de transmission consistant à sélectionner, comme le motif de transmission optimum, l'un des motifs de convergence avec la valeur d'évaluation la plus élevée obtenue à l'étape de sélection de motif de convergence,

dans lequel l'étape de sélection de motif de convergence inclut:

une étape de génération de condition initiale consistant à générer et sortir un motif initial et une valeur de graine qui est une valeur unique pour générer un nombre aléatoire comme les conditions initiales;

une étape de génération de motif consistant à générer séquentiellement le motif en changeant séquentiellement certaines des combinaisons du motif initial sorti à l'étape de génération de condition initiale sur la base du nombre aléatoire généré à partir de la valeur de graine;

une étape d'évaluation de motif consistant à calculer séquentiellement la valeur d'évaluation du motif chaque fois que le motif est sorti à l'étape de génération de motif, la valeur d'évaluation étant une valeur obtenue en divisant un débit instantané d'un équipement utilisateur de destination de transmission correspondant au point de transmission par un débit moyen sur la base d'une méthode d'équité proportionnelle;

une étape de sélection de motif candidat consistant à comparer, chaque fois que le motif est sorti à l'étape de génération de motif, la valeur d'évaluation sortie à l'étape d'évaluation de motif avec une valeur d'évaluation conservée de façon interne, sortir un motif conservé de façon interne comme un motif candidat lorsque la valeur d'évaluation sortie à l'étape d'évaluation de motif est égale ou inférieure à la valeur d'évaluation conservée de façon interne, et définir le motif sorti à l'étape de génération de motif et la valeur d'évaluation sortie à l'étape d'évaluation de motif comme le motif conservé de façon interne et la valeur d'évaluation conservée de façon interne, respectivement, lorsque la valeur d'évaluation sortie à l'étape d'évaluation de motif est supérieure à la valeur d'évaluation conservée de façon interne,

une étape de détermination de convergence consistant à calculer une quantité de changement entre les valeurs d'évaluation des deux motifs tous les deux motifs sortis à un intervalle de détermination de convergence prédéterminé parmi les motifs candidats sortis séquentiellement à l'étape de sélection de motif candidat, déterminer que les valeurs d'évaluation sont convergées vers la valeur extrême lorsque la quantité de changement obtenue est déterminée de façon continue comme étant inférieure à un seuil de détermination de convergence un nombre de fois égal à un compte de détermination de convergence, sélectionner le motif candidat comme le motif de convergence lors de la détection que la valeur d'évaluation a convergé, donner pour instruction de changer un ou les deux du motif initial et de la valeur de graine, qui sont des conditions initiales, donner pour instruction d'effectuer l'initialisation et de commencer à nouveau la génération de motif, et donner pour instruction d'initialiser le motif conservé de façon interne, dans lequel l'initialisation du motif conservé de façon interne étant la suppression des destinations de transmission de tous les points de transmission pour indiquer l'arrêt de transmission.


 




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REFERENCES CITED IN THE DESCRIPTION



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Patent documents cited in the description




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