A method and device for optimizing the rate maximization of self-organizing networks based on game theory

Abstract

The present invention discloses an optimization method and device for maximizing the rate of a self-organizing network based on game theory. The method comprises: in a hierarchical alliance self-organizing network scenario, constructing a link transmission rate model of each alliance; based on the link transmission rate model, taking the maximization of the sum of the link transmission rates of all alliances as the optimization target, and taking the association strategy of alliance members and disconnected nodes with alliance heads as variables, constructing an optimization model; based on the optimization model, adopting a distributed alliance member optimization algorithm of an alliance formation game to optimize the association between alliance members and alliance heads; based on the optimization model, adopting a link reconstruction algorithm of a bilateral matching game to optimize the association between disconnected nodes and alliance heads; the present invention can solve the technical problems of low transmission rate and communication interruption in a hierarchical alliance self-organizing network in a confrontational environment.

Description

Technical Field

[0001] The present invention relates to the field of information communication technology, and in particular to an optimization method and device for maximizing the rate of a self-organizing network based on game theory. Background Art

[0002] In a confrontational environment, reconnaissance and surveillance missions rely on a robust communication network. Pre-deployed fixed communication facilities are often destroyed due to enemy attacks and malicious interference, and cannot provide satisfactory communication services for legitimate communication nodes. Ad hoc networks have the advantages of flexible deployment and rapid mobility, and can provide efficient network connections in a confrontational environment. In particular, the hierarchical coalition-based Ad-hoc network (HCAN) is a promising network architecture. In HCAN, communication nodes are divided into multiple coalitions responsible for performing special tasks. Each coalition consists of a coalition head (CH) and several coalition members (CMs). For example Figure 1 shown.

[0003] However, HCAN still has some challenging issues to be solved in adversarial environments. First, congested spectrum resources and severe co-channel mutual interference greatly reduce the transmission rate. Second, hostile devices can launch a series of electromagnetic interference attacks on nodes, thereby rendering HCAN ineffective and causing frequent communication interruptions. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide an optimization method and device for maximizing the rate of a self-organizing network based on game theory, so as to solve the technical problems of low transmission rate and communication interruption in a hierarchical alliance self-organizing network in a confrontational environment.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention provides an optimization method for maximizing the rate of an ad hoc network based on game theory, comprising:

[0007] In the hierarchical alliance self-organizing network scenario, a link transmission rate model of each alliance is constructed;

[0008] Based on the link transmission rate model, an optimization model is constructed with maximizing the sum of link transmission rates of all alliances as the optimization goal and association strategies of alliance members and disconnected nodes with alliance heads as variables;

[0009] Based on the optimization model, a distributed alliance member optimization algorithm of the alliance formation game is used to optimize the association between alliance members and alliance heads;

[0010] Based on the optimization model, a link reconstruction algorithm of a bilateral matching game is adopted to optimize the association between disconnected nodes and alliance heads.

[0011] Optionally, the link transmission rate model is:

[0012]

[0013] In the formula, B n is the bandwidth of the nth alliance, a mn =1 means the mth alliance member joins the nth alliance, a mn =0 means that the mth alliance member has not joined the nth alliance; γ m，n , R m,n is the signal-to-interference-noise ratio and transmission rate of the link of the mth alliance member in the nth alliance;

[0014]

[0015] In the formula, p m，n ,I m，n is the link transmission power and interference signal received by the mth alliance member in the nth alliance, h m,n is the end-to-end channel gain between the mth alliance member and the alliance head in the nth alliance; is additive white Gaussian noise.

[0016] Optionally, the interference signal I m，n for:

[0017]

[0018] In the formula, is the link mutual interference signal and jammer interference signal received by the mth alliance member in the nth alliance, P i，n , P j，n is the transmission power from the i-th alliance member and the j-th jammer to the n-th alliance head, h i，n 、h j，n is the end-to-end channel gain between the i-th alliance member and the j-th jammer and the n-th alliance alliance head;

[0019] The end-to-end channel gain h m,n for:

[0020]

[0021] In the formula, is the line-of-sight component and non-line-of-sight component between the mth alliance member and the alliance head in the nth alliance, ξ and K are the Rice factor and the number of multipaths, z k = 0 means the kth multipath component is blocked, z k =1 means the kth multipath is a valid non-line-of-sight component; and are the line-of-sight component and the non-line-of-sight component, β0, d m，n , χ is the channel gain, communication distance and path loss factor when the reference distance is 1 meter.

[0022] Optionally, the optimization model is:

[0023]

[0024] In the formula, γ th is the signal-to-interference-to-noise ratio threshold, N max N is the maximum number of alliance members that the alliance can accommodate. n，c is the number of alliance members in the nth alliance.

[0025] Optionally, the distributed alliance member optimization algorithm using the alliance formation game to optimize the association between alliance members and alliance heads includes:

[0026] Initialize each alliance member, and each alliance member randomly selects an alliance within the communication range to access;

[0027] Repeat the following steps until the preset maximum number of iterations T1 is reached:

[0028] Randomly select an alliance member as the target alliance member and calculate the formation utility of the alliance it joins;

[0029] Instruct the target alliance member to reselect another alliance within the communication range, and calculate the formation utility of the reselected alliance;

[0030] If the formation utility of the alliance it reselects is greater than the formation utility of the alliance it joins, the target alliance member is made to leave the alliance it joins and join the reselected alliance.

[0031] Optionally, the formation utility is:

[0032] u m (Co n )=R[Co n ]-R[Co n \M m ]

[0033] In the formula, R[Co n ]、R[Co n \M m] is the mth alliance member M m From the nth alliance Co n Remove the sum of the transmission rates of the previous and next links from u m (Co n ) is the mth alliance member M m In the nth alliance Co n The formation utility of .

[0034] Optionally, the link reconstruction algorithm using the bilateral matching game to optimize the association between the disconnected node and the alliance head includes:

[0035] Based on the signal-to-interference-noise ratio of the link of each disconnected node in each alliance, construct a preference list of each disconnected node for the alliance head;

[0036] Repeat the following steps until the preset maximum number of iterations T2 is reached:

[0037] Randomly select an unmatched disconnected node as the target disconnected node, and make the target disconnected node send a matching request to the first alliance head in its preference list;

[0038] If the number of alliance members in the alliance corresponding to the first alliance head is N n，c =0, the target disconnected node successfully matches the first alliance header;

[0039] If the number of alliance members in the alliance corresponding to the first alliance header is 0 <N n，c <N max , then calculate the sum of the link transmission rates before and after the target disconnected node accesses the alliance corresponding to the first alliance header; if the sum of the link transmission rates after access is greater than the sum of the link transmission rates before access, the target disconnected node successfully matches the first alliance header, otherwise the first alliance header is deleted from the preference list of the target disconnected node; N max The maximum number of alliance members that the alliance can accommodate;

[0040] If the number of alliance members in the alliance corresponding to the first alliance head is N n，c =N max , then replace one alliance member in the alliance corresponding to the first alliance head with the target disconnected node to form N max new alliance, calculate the alliance and N corresponding to the first alliance head max The sum of the link transmission rates of the new alliances. If the sum of the link transmission rates of the alliance corresponding to the first alliance head is the largest, the first alliance head is deleted from the preference list of the target disconnected node. If N maxIf the sum of link transmission rates of the s-th new alliance in the s-th new alliance is the largest, the target disconnected node successfully matches the first alliance head, and the alliance member replaced by the target disconnected node corresponding to the s-th new alliance is removed; the removed alliance member is used as the disconnected node, and its preference list for the alliance head is constructed.

[0041] Optionally, the optimizing the association between the disconnected node and the alliance head further includes:

[0042] Repeat the following steps until the preset maximum number of iterations T3 is reached:

[0043] Randomly select two groups of successfully matched disconnected nodes and alliance heads (M m1 , N n1 )、(M m2 , N n2 );

[0044] If satisfied:

[0045] R[Co n1 \M m1 ∪M m2 ]+R[Co n2 / M m2 ∪M m1 ]>R[Co n1 ]+R[Co n2 ]

[0046] Then, the reconnected node and alliance head are (M m1 , N n2 )、(M m2 , N n1 );

[0047] In the formula, R[Co n1 \M m1 ∪M m2 ] is to remove the m1th alliance member M m1 And join the m2th alliance member M m2 The n1th alliance Co n1 The sum of the link transmission rates; R[Co n2 \M m2 ∪M m1 ] is to remove the m2th alliance member M m2 And join the m1th alliance member M m1 The n2th alliance Co n2 The sum of the link transmission rates; R[Co n1 ]、R[Co n2 ] is the n1th alliance Co n1 、The n2th alliance Co n2 The sum of the link transmission rates.

[0048] In a second aspect, the present invention provides an optimization device for maximizing the rate of an ad hoc network based on game theory, the device comprising:

[0049] The rate model building module is used to build the link transmission rate model of each alliance in the hierarchical alliance self-organizing network scenario;

[0050] An optimization module construction module is used to construct an optimization model based on the link transmission rate model, taking the maximization of the sum of the link transmission rates of all alliances as the optimization goal, and taking the association strategy of the alliance members and the disconnected nodes with the alliance head as variables;

[0051] A member distribution optimization module, used to optimize the association between alliance members and alliance heads based on the optimization model and using a distributed alliance member optimization algorithm of the alliance formation game;

[0052] The link reconstruction optimization module is used to optimize the association between disconnected nodes and alliance heads based on the optimization model and using a link reconstruction algorithm of a bilateral matching game.

[0053] In a third aspect, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon, which implements the steps of the above method when the computer program / instruction is executed by a processor.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The present invention provides an optimization method and device for maximizing the rate of a self-organizing network based on game theory. The method can be divided into two stages. The first stage optimizes the composition of alliance members to obtain a stable alliance division to reduce co-frequency mutual interference and enemy malicious interference. The second stage uses a bilateral matching game to reconstruct the disconnected link. Compared with existing work, the proposed link access method based on game theory can improve the sum rate of the self-organizing network in a distributed manner and ensure the stability of communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a structural diagram of a hierarchical self-organizing network system model provided by the background technology of the present invention;

[0057] Figure 2 It is a flow chart of an optimization method for maximizing the rate of an ad hoc network based on game theory provided by an embodiment of the present invention;

[0058] Figure 3 It is a schematic diagram of a process of optimizing association between alliance members and alliance heads provided by an embodiment of the present invention;

[0059] Figure 4 It is a schematic diagram of a process of optimizing the association between a disconnected node and an alliance head provided by an embodiment of the present invention;

[0060] Figure 5 is a performance comparison diagram of an algorithm based on the alliance formation game provided by an embodiment of the present invention;

[0061] Figure 6 This is a performance comparison diagram of algorithms based on matching game provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0063] Embodiment 1:

[0064] like Figure 2 As shown, an embodiment of the present invention provides an optimization method for maximizing the rate of an ad hoc network based on game theory, comprising the following steps:

[0065] Step S1: In a hierarchical alliance self-organizing network scenario, construct a link transmission rate model for each alliance;

[0066] In the considered HCAN scenario, the set of alliance members and alliance heads is denoted as M = {M1, M2, … M M} and N={Co1,Co2,…Co N The alliances use frequency division multiple access technology to allocate orthogonal spectrum resources to each alliance, so there is no co-frequency interference between alliances. Spectrum resources are shared within the same alliance. The alliance head is responsible for managing, allocating and coordinating communication resources within the alliance. Each alliance member is connected to the upper-level alliance head through a device-to-device (D2D) link. Alliance heads are connected to each other and exchange information. n The bandwidth is B n , all members of the same alliance share the spectrum resources within the alliance. Therefore, the signal-to-interference-plus-noise ratio (SINR) of the link in the alliance is affected by the co-frequency interference of other nodes within the alliance, resulting in a decrease in the transmission rate. In addition, enemy interference opportunities will interfere with legitimate links.

[0067] Therefore, the SINR of the links within the alliance can be expressed as:

[0068]

[0069] Where P m,n ,I m，n is the link transmission power and interference signal received by the mth alliance member in the nth alliance, h m,nis the end-to-end channel gain between the mth alliance member and the alliance head in the nth alliance; is additive white Gaussian noise.

[0070] Interference signal I m，n for:

[0071]

[0072] In the formula, is the link mutual interference signal and jammer interference signal received by the mth alliance member in the nth alliance, P i，n , P j，n is the transmission power from the i-th alliance member and the j-th jammer to the n-th alliance head, h i，n 、h j，n is the end-to-end channel gain between the i-th coalition member and the j-th jammer and the n-th coalition coalition head.

[0073] End-to-end channel gain h m,n for:

[0074]

[0075]

[0076] In the formula, is the line-of-sight component and non-line-of-sight component between the m-th alliance member and the alliance head in the n-th alliance, (, K is the Rice factor and the number of multipaths, z k = 0 means the kth multipath component is blocked, z k =1 means the kth multipath is a valid non-line-of-sight component; and are the line-of-sight component and the non-line-of-sight component, β0, d m,n , χ is the channel gain, communication distance and path loss factor when the reference distance is 1 meter.

[0077] The link transmission rate model is:

[0078]

[0079] In the formula, B n is the bandwidth of the nth alliance, a mn =1 means the mth alliance member joins the nth alliance, a mn =0 means that the mth alliance member has not joined the nth alliance; γ m，n , R m,n is the signal to noise ratio and transmission rate of the link of the mth alliance member in the nth alliance.

[0080] Step S2: Based on the link transmission rate model, the optimization goal is to maximize the sum of the link transmission rates of all alliances, and the association strategies of alliance members and disconnected nodes with the alliance head are used as variables to construct an optimization model; the optimization model is:

[0081]

[0082] As the number of nodes increases, co-channel interference will continue to increase. In order to reduce self-interference within the alliance, alliance members can leave the current alliance and choose to join a new alliance to reduce the alliance's self-interference and enemy malicious interference, thereby increasing the alliance's system capacity. For disconnected nodes, you can choose to join a new alliance for link reconstruction. Therefore, the system capacity of HCAN can be improved by optimizing the connection relationship between the alliance head and alliance members (or disconnected nodes). In order to meet the performance requirements of the receiver, the SINR constraint can be expressed as:

[0083]

[0084] In the formula, Yt h is the signal-to-interference-noise ratio threshold;

[0085] In addition, due to the limited processing capacity of each alliance head, each alliance can only accommodate up to N max members. In addition, each alliance member can only access one alliance. Therefore, the constraint can be expressed as:

[0086]

[0087] Where N max N is the maximum number of alliance members that the alliance can accommodate. n，c is the number of alliance members in the nth alliance.

[0088] Step S3: Based on the optimization model, a distributed alliance member optimization algorithm of the alliance formation game is used to optimize the association between alliance members and alliance heads.

[0089] In the embodiment, by changing the composition of alliance members, mutual channel interference and enemy interference can be mitigated, and the problem of switching alliances among self-organizing network nodes is modeled as an alliance formation game. All alliance members are divided into non-overlapping alliances, and each alliance member M m Has its own preference criteria > m The utility function of each alliance member is designed according to the goal of alliance formation. In this embodiment, the M m The coalition formation utility function u m (Co n ) is defined as M m Join AllianceCo n The link transmission rate is increased after

[0090] u m (Co n )=R[Co n ]-R[Co n \M m ]

[0091] In the formula, R[Co n ]、R[Co n \M m ] is the mth alliance member M m From the nth alliance Co n Remove the sum of the transmission rates of the previous and next links from u m (Co n ) is the mth alliance member M m In the nth alliance Co n The formation utility of .

[0092] The convergence performance of the proposed game and the final alliance division depend on the preference relationship of the alliance. This embodiment proposes a bilateral optimal preference criterion. If the alliance member M m After the alliance switch occurs, the new alliance Co n2 and Old Alliance Co n1 The sum of the rates of M is greater than the sum of the rates before switching. m Choose to leave the old alliance Co n1 , and join the new alliance Co n2 ,Right now

[0093]

[0094] For the bilateral optimal criterion, the sum of the rates of the new alliance and the old alliance will increase as the alliance members switch to their alliances. This example uses potential energy game to prove the convergence of the alliance formation game. The definition of potential energy game is as follows: if the potential energy function satisfies the following formula, the game model is a potential energy game, and there is at least one Nash equilibrium solution.

[0095] φ(Co n2 ,Co -n2 )-φ(Co n1 ,Co -n1 )=u m (Co n2 )-u m (Co n1 )

[0096] Where Co -n2 For all except M m Alliance selection by other alliance members.

[0097] In this embodiment, the potential energy function is set to When alliance member M m The alliance selected by Co n1 Become Co n2 When , the increment of the potential energy function can be written as:

[0098]

[0099] Since alliance member M m The change of the alliance selection strategy will only affect the transmission rate of the current alliance and the new alliance, but will not change the transmission rate of other alliances, that is,

[0100]

[0101] The increment of the potential energy function can be further written as

[0102] φ(Co n2 ,Co -n2 )-φ(Co n1 ,Co -n1 )=R[Co n2 ∪M m ]+R[Co n1 \M m ]-R[Co n2 ]-R[Co n1 ]=R[Co n2 ∪M m ]--R[Co n2 ]-(R[Co n1 ]-R[Co n1 \M m ])=u m (Co n2 )-u m (Co n1 )

[0103] It can be found that when alliance member M m The alliance chooses from Co n1 Become Co n2 When , the increment of the potential energy function is equal to the increment of the utility function of coalition formation. Therefore, the proposed coalition game based on the bilateral optimal criterion is an exact potential energy game and there is at least one Nash equilibrium solution. When each coalition member changes its coalition to increase utility, the potential energy function will increase. Since the number of coalition heads and coalition members is finite, the possible results of coalition partition are finite. After a sufficient number of iterations, the coalition partition with the highest potential energy function will be obtained. Finally, if no coalition member can improve its utility function by unilaterally changing its current coalition, a stable coalition partition is formed. Since the potential energy function is also the objective function of the optimization problem, the sum rate of stable coalition formation is the suboptimal solution to the optimization objective.

[0104] like Figure 3 As shown, a distributed alliance member optimization algorithm based on alliance formation game is used to optimize the association between alliance members and alliance heads. The specific steps include:

[0105] Step S301: Initialize each alliance member, and each alliance member randomly selects an alliance within the communication range to access;

[0106] Step S302: Repeat the following steps until the preset maximum number of iterations T1 is reached:

[0107] Step S303: randomly select an alliance member as a target alliance member, and calculate the formation utility of the alliance it joins;

[0108] Step S304: Instruct the target alliance member to reselect another alliance within the communication range, and calculate the formation utility of the reselected alliance;

[0109] Step S305: If the formation utility of the alliance it reselects is greater than the formation utility of the alliance it joins, the target alliance member is ordered to leave the alliance it joins and join the reselected alliance.

[0110] Step S4: Based on the optimization model, a link reconstruction algorithm of a bilateral matching game is used to optimize the association between disconnected nodes and alliance heads.

[0111] After a stable alliance is formed with the goal of maximizing the link transmission rate of the global network, some alliance members will be disconnected from the currently connected alliance head because the links between nodes are affected by factors such as terrain, mobility, and enemy intelligent interference attacks. The disconnected node needs to reselect a new alliance to access in order to quickly restore the link connection. Since the disconnected nodes and alliance heads have asymmetric information acquisition capabilities, this paper models the link reconstruction problem of disconnected nodes as an asymmetric many-to-one matching game.

[0112] First, from the perspective of matching the needs of both parties, build their respective preference lists.

[0113] The target of a disconnected node is the access rate of the node:

[0114]

[0115] The objective function of the alliance head is to improve the network and rate of the alliance it leads, so the disconnected nodes that can bring higher network and rate to the alliance have higher priority, that is:

[0116]

[0117] Where N n2 、N n1 For Alliance Co n2 、Con1 The Alliance Head, m 、> n For alliance member M m and Union Head N n Preference. m2 > n M m1 Indicates the alliance header N n Prefer M m2 , N n2 > m N n1 Indicates alliance member M m Prefer alliance head N n2 .

[0118] The classic rejection algorithm divides the two parties of the matching game into applicants and acceptors, which can effectively solve the bilateral matching problem. In this embodiment, the disconnected node, as the applicant, first initiates a matching request to the alliance head in turn according to its respective preference list. The alliance head accepts the disconnected node with the highest ranking according to its own preference list and rejects the requests of other nodes. The disconnected node that failed to match successfully continues to initiate an application to the next alliance head in the preference list, and each alliance head optimizes the matching selection based on the preference list. Since the number of disconnected nodes and alliance heads is limited, the preference list is also limited. After multiple bilateral selections, the matching parties can obtain stable bilateral matching results after a limited number of iterations.

[0119] like Figure 4 As shown in FIG. 1 , the link reconstruction algorithm of the bilateral matching game is used to optimize the association between the disconnected node and the alliance head, including:

[0120] Step S401: construct a preference list of each disconnected node for the alliance head based on the signal-to-interference-noise ratio of the link of each disconnected node in each alliance;

[0121] Step S402: Repeat the following steps until the preset maximum number of iterations T2 is reached:

[0122] Step S403: randomly select an unmatched disconnected node as a target disconnected node, and instruct the target disconnected node to send a matching request to the first alliance header in its preference list;

[0123] Step S404: If the number of alliance members in the alliance corresponding to the first alliance header is N n,c =0, the target disconnected node successfully matches the first alliance header;

[0124] Step S405: If the number of alliance members in the alliance corresponding to the first alliance header is 0 <N n,c <N max, then calculate the sum of the link transmission rates before and after the target disconnected node accesses the alliance corresponding to the first alliance header; if the sum of the link transmission rates after access is greater than the sum of the link transmission rates before access, the target disconnected node successfully matches the first alliance header, otherwise the first alliance header is deleted from the preference list of the target disconnected node; N max The maximum number of alliance members that the alliance can accommodate;

[0125] Step S406: If the number of alliance members in the alliance corresponding to the first alliance header is N n,c =N max , then replace one alliance member in the alliance corresponding to the first alliance head with the target disconnected node to form N max new alliance, calculate the alliance and N corresponding to the first alliance head max The sum of the link transmission rates of the new alliances. If the sum of the link transmission rates of the alliance corresponding to the first alliance head is the largest, the first alliance head is deleted from the preference list of the target disconnected node. If N max If the sum of the link transmission rates of the sth new alliance in the new alliance is the largest, the target disconnected node matches the first alliance head successfully, and the alliance member replaced by the target disconnected node corresponding to the sth new alliance is removed; the removed alliance member is regarded as the disconnected node, and its preference list for the alliance head is constructed.

[0126] It is worth noting that refusing to accept that the matching results obtained by the algorithm are stable rather than optimal.

[0127] This paper further uses the idea of ​​local mutual benefit to optimize the rejection algorithm, that is, for the obtained matching results, if the two sets of matching results can improve the total sum rate after exchanging matching objects, then the matching objects are exchanged, otherwise the original matching results are maintained, that is: the association optimization of disconnected nodes and alliance heads also includes:

[0128] Repeat the following steps until the preset maximum number of iterations T3 is reached:

[0129] Randomly select two groups of successfully matched disconnected nodes and alliance heads (M m1 , N n1 )、(M m2 , N n2 );

[0130] If satisfied:

[0131] R[Co n1 \M m1 ∪M m2 ]+R[Co n2 \M m2 ∪M m1 ]>R[Co n1 ]+R[Co n2 ]

[0132] Then, the reconnected node and alliance head are (M m1 , N n2 )、(M m2 , N n1 );

[0133] In the formula, R[Co n1 \M m1 ∪M m2 ] is to remove the m1th alliance member M m1 And join the m2th alliance member M m2 The n1th alliance Co n1 The sum of the link transmission rates; R[Co n2 \M m2 ∪M m1 ] is to remove the m2th alliance member M m2 And join the m1th alliance member M m1 The n2th alliance Co n2 The sum of the link transmission rates; R[Co n1 ]、R[Co n2 ] is the n1th alliance Co n1 、The n2th alliance Co n2 The sum of the link transmission rates.

[0134] Figure 5 The performance of the algorithms under different alliance preference orders is compared. As can be seen from the figure, compared with the traditional selfish and Pareto criteria, the proposed bilateral optimal criterion can obtain the highest sum rate. The selfish criterion only focuses on the utility of the alliance members themselves when selecting a new alliance, which may damage the utility of other alliance members while improving individual utility. For the Pareto criterion, in order to ensure that the benefits of the current alliance and the new alliance are not damaged, the alliance changes of the Pareto criterion are strictly restricted, resulting in a lower sum rate than the bilateral optimal criterion.

[0135] Figure 6 The proposed algorithm is compared with the access scheme based on minimum distance and the blind access scheme. For the access scheme based on minimum distance, the disconnected node only pays attention to the communication distance and selects the alliance with the shortest access distance. In the blind access scheme, the disconnected node randomly accesses the alliance without considering the influence of channel gain and co-channel interference. The sum rate of the proposed scheme gradually increases with the increase of the number of iterations, and the performance is better than other schemes. The scheme can consider the communication requirements of the disconnected node and the alliance head at the same time, effectively improving the sum rate of the ad hoc network.

[0136] Embodiment 2:

[0137] The embodiment of the present invention provides an optimization device for maximizing the rate of an ad hoc network based on game theory, the device comprising:

[0138] The rate model building module is used to build the link transmission rate model of each alliance in the hierarchical alliance self-organizing network scenario;

[0139] The optimization module is used to construct an optimization model based on the link transmission rate model, taking the maximization of the sum of the link transmission rates of all alliances as the optimization goal, and taking the association strategies of alliance members and disconnected nodes with the alliance head as variables;

[0140] The member distribution optimization module is used to optimize the association between alliance members and alliance heads based on the optimization model and the distributed alliance member optimization algorithm of the alliance formation game;

[0141] The link reconstruction optimization module is used to optimize the association between disconnected nodes and alliance heads based on the optimization model and the link reconstruction algorithm of the bilateral matching game.

[0142] Embodiment three:

[0143] Based on the first embodiment, the embodiment of the present invention provides a computer-readable storage medium, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps of the above method are implemented.

[0144] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0148] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An optimization method for maximizing the rate of a self-organizing network based on game theory, characterized in that: include: In the hierarchical alliance self-organizing network scenario, a link transmission rate model of each alliance is constructed; Based on the link transmission rate model, an optimization model is constructed with maximizing the sum of link transmission rates of all alliances as the optimization goal and association strategies of alliance members and disconnected nodes with alliance heads as variables; Based on the optimization model, a distributed alliance member optimization algorithm of the alliance formation game is used to optimize the association between alliance members and alliance heads; Based on the optimization model, a link reconstruction algorithm of a bilateral matching game is used to optimize the association between disconnected nodes and alliance heads; The link reconstruction algorithm using the bilateral matching game to optimize the association between the disconnected node and the alliance head includes: Based on the signal-to-interference-noise ratio of the link of each disconnected node in each alliance, construct a preference list of each disconnected node for the alliance head; Repeat the following steps until the preset maximum number of iterations T2 is reached: Randomly select an unmatched disconnected node as the target disconnected node, and make the target disconnected node send a matching request to the first alliance head in its preference list; If the number of alliance members in the alliance corresponding to the first alliance head is N n,c =0, the target disconnected node successfully matches the first alliance header; If the number of alliance members in the alliance corresponding to the first alliance header is 0 <N n,c <N max , then calculate the sum of the link transmission rates before and after the target disconnected node accesses the alliance corresponding to the first alliance header; if the sum of the link transmission rates after access is greater than the sum of the link transmission rates before access, the target disconnected node successfully matches the first alliance header, otherwise the first alliance header is deleted from the preference list of the target disconnected node; N max The maximum number of alliance members that the alliance can accommodate; If the number of alliance members in the alliance corresponding to the first alliance head is N n,c =N max , then replace one alliance member in the alliance corresponding to the first alliance head with the target disconnected node to form N max new alliance, calculate the alliance and N corresponding to the first alliance head max The sum of the link transmission rates of the new alliances. If the sum of the link transmission rates of the alliance corresponding to the first alliance head is the largest, the first alliance head is deleted from the preference list of the target disconnected node. If N max If the sum of the link transmission rates of the sth new alliance in the sth new alliance is the largest, the target disconnected node is successfully matched with the first alliance head, and the alliance member replaced by the target disconnected node corresponding to the sth new alliance is removed; the removed alliance member is used as the disconnected node, and its preference list for the alliance head is constructed; The optimization of the association between the disconnected node and the alliance head also includes: Repeat the following steps until the preset maximum number of iterations T3 is reached: Randomly select two groups of successfully matched disconnected nodes and alliance heads (M m1 ,N n1 )、(M m2 ,N n2 ); If satisfied: R[Co n1 \M m1 ∪M m2 ]+R[Co n2 / M m2 ∪M m1 ]>R[What n1 ]+R[Co n2 ] Then, the reconnected node and alliance head are (M m1 ,N n2 )、(M m2 ,N n1 ); In the formula, R[Co n1 \M m1 ∪M m2 ] is to remove the m1th alliance member M m1 And join the m2th alliance member M m2 The n1th alliance Co n1 The sum of the link transmission rates; R[Co n2 \M m2 ∪M m1 ] is to remove the m2th alliance member M m2 And join the m1th alliance member M m1 The n2th alliance Co n2 The sum of the link transmission rates; R[Co n1 ]、R[Co n2 ] is the n1th alliance Co n1 、n2th alliance Co n2 The sum of the link transmission rates.

2. The optimization method for maximizing the rate of a self-organizing network based on game theory according to claim 1, characterized in that: The link transmission rate model is: In the formula, B n is the bandwidth of the nth alliance, a mn =1 means the mth alliance member joins the nth alliance, a mn =0 means that the mth alliance member has not joined the nth alliance; γ m,n , R m,n is the signal-to-interference-noise ratio and transmission rate of the link of the mth alliance member in the nth alliance; In the formula, p m,n ,I m,n is the link transmission power and interference signal received by the mth alliance member in the nth alliance, h m,n is the end-to-end channel gain between the mth alliance member and the alliance head in the nth alliance; is additive white Gaussian noise.

3. The optimization method for maximizing the rate of a self-organizing network based on game theory according to claim 2 is characterized in that: The interference signal I m,n for: In the formula, is the link mutual interference signal and jammer interference signal received by the mth alliance member in the nth alliance, P i,n , P j,n is the transmission power from the i-th alliance member and the j-th jammer to the n-th alliance head, h i,n 、h j,n is the end-to-end channel gain between the i-th alliance member and the j-th jammer and the n-th alliance alliance head; The end-to-end channel gain h m,n for: In the formula, is the line-of-sight component and non-line-of-sight component between the mth alliance member and the alliance head in the nth alliance, ξ and K are the Rice factor and the number of multipaths, z k = 0 means the kth multipath component is blocked, z k =1 means the kth multipath is a valid non-line-of-sight component; and are the line-of-sight component and the non-line-of-sight component, β0, d m,n , X is the channel gain, communication distance and path loss factor when the reference distance is 1 meter.

4. The optimization method for maximizing the rate of a self-organizing network based on game theory according to claim 2, characterized in that: The optimization model is: In the formula, γ th is the signal-to-interference-noise ratio threshold, N max N is the maximum number of alliance members that the alliance can accommodate. n,c is the number of alliance members in the nth alliance.

5. The optimization method for maximizing the rate of a self-organizing network based on game theory according to claim 1, characterized in that: The distributed alliance member optimization algorithm using the alliance formation game to optimize the association between alliance members and alliance heads includes: Initialize each alliance member, and each alliance member randomly selects an alliance within the communication range to access; Repeat the following steps until the preset maximum number of iterations T1 is reached: Randomly select an alliance member as the target alliance member and calculate the formation utility of the alliance it joins; Instruct the target alliance member to reselect another alliance within the communication range, and calculate the formation utility of the reselected alliance; If the formation utility of the alliance it reselects is greater than the formation utility of the alliance it joins, the target alliance member is made to leave the alliance it joins and join the reselected alliance.

6. The optimization method for maximizing the rate of a self-organizing network based on game theory according to claim 5, characterized in that: The formation utility is: at m (What n )=R[Co n ]-R[Co n \M m ] In the formula, R[Co n ]、R[Co n \M m ] is the mth alliance member M m From the nth alliance Co n Remove the sum of the transmission rates of the previous and next links from u m (Co n ) is the mth alliance member M m In the nth alliance Co n The formation utility of .

7. An optimization device for maximizing the rate of a self-organizing network based on game theory, characterized in that: The device comprises: The rate model building module is used to build the link transmission rate model of each alliance in the hierarchical alliance self-organizing network scenario; An optimization module construction module is used to construct an optimization model based on the link transmission rate model, taking the maximization of the sum of the link transmission rates of all alliances as the optimization goal, and taking the association strategy of the alliance members and the disconnected nodes with the alliance head as variables; A member distribution optimization module, used to optimize the association between alliance members and alliance heads based on the optimization model and using a distributed alliance member optimization algorithm of the alliance formation game; A link reconstruction optimization module, used to optimize the association between disconnected nodes and alliance heads by using a link reconstruction algorithm of a bilateral matching game based on the optimization model; The link reconstruction algorithm using the bilateral matching game to optimize the association between the disconnected node and the alliance head includes: Based on the signal-to-interference-noise ratio of the link of each disconnected node in each alliance, construct a preference list of each disconnected node for the alliance head; Repeat the following steps until the preset maximum number of iterations T2 is reached: Randomly select an unmatched disconnected node as the target disconnected node, and make the target disconnected node send a matching request to the first alliance head in its preference list; If the number of alliance members in the alliance corresponding to the first alliance head is N n,c =0, the target disconnected node successfully matches the first alliance header; If the number of alliance members in the alliance corresponding to the first alliance header is 0 <N n,c <N max , then calculate the sum of the link transmission rates before and after the target disconnected node accesses the alliance corresponding to the first alliance header; if the sum of the link transmission rates after access is greater than the sum of the link transmission rates before access, the target disconnected node successfully matches the first alliance header, otherwise the first alliance header is deleted from the preference list of the target disconnected node; N max The maximum number of alliance members that the alliance can accommodate; If the number of alliance members in the alliance corresponding to the first alliance head is N n,c =N max , then replace one alliance member in the alliance corresponding to the first alliance head with the target disconnected node to form N max new alliance, calculate the alliance and N corresponding to the first alliance head max The sum of the link transmission rates of the new alliances. If the sum of the link transmission rates of the alliance corresponding to the first alliance head is the largest, the first alliance head is deleted from the preference list of the target disconnected node. If N max If the sum of the link transmission rates of the sth new alliance in the sth new alliance is the largest, the target disconnected node is successfully matched with the first alliance head, and the alliance member replaced by the target disconnected node corresponding to the sth new alliance is removed; the removed alliance member is used as the disconnected node, and its preference list for the alliance head is constructed; The optimization of the association between the disconnected node and the alliance head also includes: Repeat the following steps until the preset maximum number of iterations T3 is reached: Randomly select two groups of successfully matched disconnected nodes and alliance heads (M m1 ,N n1 )、(M m2 ,N n2 ); If satisfied: R[Co n1 \M m1 ∪M m2 ]+R[Co n2 / M m2 ∪M m1 ]>R[What n1 ]+R[Co n2 ] Then, the reconnected node and alliance head are (M m1 ,N n2 )、(M m2 ,N n1 ); In the formula, R[Co n1 \M m1 ∪M m2 ] is to remove the m1th alliance member M m1 And join the m2th alliance member M m2 The n1th alliance Co n1 The sum of the link transmission rates; R[Co n2 \M m2 ∪M m1 ] is to remove the m2th alliance member M m2 And join the m1th alliance member M m1 The n2th alliance Co n2 The sum of the link transmission rates; R[Co n1 ]、R[Co n2 ] is the n1th alliance Co n1 、n2th alliance Co n2 The sum of the link transmission rates.

8. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of any method described in claims 1-6 are implemented.

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