D2D communication resource allocation method based on chaos perception game

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to a D2D communication resource allocation method based on chaos perception game, which comprises the following steps: S1, data input: initializing user position information, and calculating related parameters such as channel gain, signal-to-noise ratio, throughput and the like among users; s2, mathematical modeling: broadcasting information of each user by a base station, establishing a chaotic game framework, and calculating a utility function of each user; according to the method, the game theory and the chaos theory are fused, so that the D2D communication resource allocation problem is solved. The chaos perception game adapts to rapid change of a network state by considering nonlinearity and unpredictability of a system, and a resource allocation strategy is adjusted in time. The dynamic adaptability is particularly important in a highly-moving or high-density D2D network. A traditional game theory always pays attention to local equilibrium and neglects a global optimal solution. The chaotic perception game can explore and approach a globally optimal solution by introducing chaotic dynamics, so that the optimal configuration of resources is realized on a system level.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a D2D communication resource allocation method based on chaos perception game. Background Art

[0002] In modern wireless communication systems, device-to-device (D2D) communication has attracted widespread attention as a key technology that can significantly improve spectrum efficiency and network capacity. In traditional cellular networks, communication devices are usually transferred through base stations, while D2D communication allows user equipment (UE) to communicate directly, which not only reduces latency but also effectively reduces the load on base stations. However, the introduction of D2D communication also brings new challenges, especially in resource allocation.

[0003] Resource allocation is one of the core issues in D2D communication, because D2D communication devices need to share limited wireless resources with cellular users. In order to ensure the quality and efficiency of communication, spectrum resources, power and other network resources must be reasonably allocated to maximize the overall performance of the system. Traditional resource allocation methods mostly rely on centralized control strategies, however, these methods often have problems such as high complexity, high computational overhead and inability to adapt to dynamic network environments.

[0004] Game theory, as a mathematical tool for studying the strategic interactions between multiple participants, has been widely used in resource allocation problems in wireless communications. Through game theory, devices can be modeled as self-interested participants, and the goal of each device is to maximize its own utility by selecting the optimal strategy. However, traditional game theory methods may have difficulty adapting to environmental changes quickly when faced with a highly dynamic and uncertain D2D communication environment, thus affecting the efficiency and fairness of resource allocation. In this context, chaos theory introduces a new dimension. Chaos theory emphasizes the nonlinear behavior and sensitivity of initial conditions in the system, which is naturally consistent with the dynamic characteristics of D2D networks. Therefore, how to use chaos-aware game methods to better capture the potential complex dynamic patterns in D2D communications and thus perform more efficient resource allocation remains a challenging problem. Therefore, research on D2D resource allocation methods based on chaos-aware games has strong practical value. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In view of the deficiencies of the prior art, the present invention provides a D2D communication resource allocation method based on chaos-aware game, which solves the problems raised in the above background technology.

[0007] (II) Technical solution

[0008] In order to achieve the above-mentioned purpose, the present invention specifically adopts the following technical solutions:

[0009] A D2D communication resource allocation method based on chaos perception game includes the following steps:

[0010] S1: Data input: Initialize user location information, calculate channel gain, signal-to-noise ratio, throughput and other related parameters between users;

[0011] S2: Mathematical modeling: The base station broadcasts each user's information, establishes a chaotic game framework, and calculates each user's utility function;

[0012] S3: Strategy selection: Select the game strategy based on the information perception algorithm;

[0013] S4: Iterative update: Iterate the cycle to reach Nash equilibrium;

[0014] S5: Data output: Output the channel selection and power allocation results of each user.

[0015] Furthermore, the channel gain in S1 is a key factor to be considered in the signal transmission and power allocation process. The channel gain model in the communication system is analyzed below; the channel gain between the base station and the cellular user is g kb ; The channel gain between the base station and the D2D user is g ib ; The channel gain between cellular users and D2D users is g ki ; The channel gain between the D2D user transmitter and receiver is g ij ; Therefore, the signal-to-noise ratio expressions of cellular user i and D2D user j are:

[0016]

[0017] The total system throughput is expressed as:

[0018] The system model is a mode in which multiple D2D users reuse the same cellular user channel resources, referred to as the "many-to-one" mode. The goal is to maximize the total system throughput under various constraints.

[0019] Furthermore, the chaotic game framework is established in S2 to calculate the utility function of each user, and the resource reuse position of the D2D user is optimized by generating multiple candidate solutions in the Sierpinski triangle, wherein each candidate solution represents a different optimization decision variable matrix, and its element a i,j Represents the specific location of the D2D user in the search space; the initial location is randomly distributed in a triangular area, and the formula Calculation is performed; afterwards, the candidate solution is dynamically adjusted through control parameters to ensure the optimal allocation of resources. These adjustment steps reflect the self-organizing behavior of D2D users in dynamic nonlinear systems and the self-similar patterns existing in the system. In order to suppress the vicious competition in non-cooperative games, a utility function can be introduced so that users can pay the price for the negative impact of their behavior while gaining benefits, thereby achieving self-optimization without affecting others, and promoting implicit cooperative behavior in the game to improve the overall efficiency of the system. The utility functions of cellular users and D2D users can be expressed as:

[0020]

[0021] When a user causes greater interference to users with the same frequency, the price paid by the user is also greater; however, when a user is interfered with more by users with the same frequency, the price paid by the user also becomes smaller. Therefore, the utility function can suppress vicious competition among users.

[0022] Furthermore, in S3, the game strategy is selected according to the information perception algorithm. The information perception algorithm is an improved algorithm based on the heuristic algorithm, which includes a three-stage optimization process: information collection stage, information perception and evaluation stage, and information analysis stage. In the information collection stage, the process of human beings collecting information from various sources is simulated to construct an initial solution set. In the information perception and evaluation stage, the information is perceived and evaluated according to predefined standards, redundant information is eliminated, and information with potential is retained. In the stage of analyzing information, the optimal solution is identified.

[0023] S3.1: Initialize the information system and collect comprehensive information. This process can be described as follows:

[0024]

[0025] S3.2: The mathematical modeling of the information perception algorithm exploration phase is:

[0026]

[0027] rand is a randomly generated number, and Δ is the error caused by subjective factors when filtering and evaluating information, which is defined by the following formula:

[0028]

[0029] Where Ξ is the subjective influence factor, which plays a key role as a quantitative indicator of individual subjectivity. It illustrates the uncertainty inherent in filtering and evaluating information, which means that even if the same information is provided, the system may evaluate different user information or the same user at different time points differently due to changes in subjective states. The calculation formula of the subjective influence factor is as follows:

[0030] Ξ=2×mod(3.468×ν×(1-β)×(acos(γ×10 4 ),1)

[0031] Γ is defined as a reliability factor. The inspiration for constructing its mathematical model comes from the ability of humans to dynamically adjust the filtering and evaluation process when processing information. As the processing progresses, the information gradually changes from complex to simple, and the mathematical model is gradually simplified; therefore, Γ reflects the ability of the algorithm to optimize its behavior by self-adjusting according to the quality of information at different iteration stages. This design greatly enhances the adaptability and flexibility of the algorithm and effectively improves the reliability and accuracy of the equation calculation results. The calculation formula of the reliability factor is as follows:

[0032]

[0033] The exponential processing of the sine function provides a dynamic adjustment mechanism for the algorithm. In the initial stage, the changes are significant, and the screening strategy can be adjusted quickly. In the later stage, the rate of change gradually slows down, which is conducive to the stability and fine-tuning of the algorithm. The logarithmic part ensures the stability of the iterative process and smoothes the impact as the number of iterations increases, simulating the transition process of information processing from coarse screening to fine screening. The information quality factor Φ ensures the key role of information quality in affecting Γ, preventing the algorithm from ignoring the basic requirements for information quality when over-optimizing the iterative dynamics.

[0034] S3.3: Information analysis phase: The algorithm is dynamically optimized based on the quality and reliability of the information, thereby significantly improving the accuracy and effectiveness of the final information. This adaptive adjustment mechanism enables the algorithm to conduct deeper information mining and broader information integration. In the next iteration, the new information subject will completely replace the old information subject.

[0035] Furthermore, the iterative loop in S4 to reach Nash equilibrium includes the following steps:

[0036] S4.1: In a non-cooperative game, for each player, the strategy space is a non-empty and closed convex set, the utility function is a continuous function in the strategy space, and the utility function is a quasi-concave function, then the non-cooperative game has at least one Nash equilibrium solution;

[0037] S4.2: Determine the best response function based on the chaotic game framework and information perception algorithm. The utility function describes the benefits that participants can obtain from different strategy choices in the game, while the best response function is to find the strategy choice that maximizes the utility function given the strategies of other participants. The utility function is the basis for participants to optimize their strategies, and the best response function reflects the results of this optimization.

[0038] S4.3: Prove the positivity, monotonicity, and scalability of the best response function to determine that the Nash equilibrium solution of the non-cooperative game model is unique, and the Nash equilibrium solution is the optimal solution for power allocation and channel selection for each user.

[0039] (III) Beneficial effects

[0040] Compared with the prior art, the present invention provides a D2D communication resource allocation method based on chaos perception game, which has the following beneficial effects:

[0041] The present invention solves the D2D communication resource allocation problem by integrating game theory and chaos theory. Chaos-aware games adapt to the rapid changes in network status and adjust resource allocation strategies in a timely manner by considering the nonlinearity and unpredictability of the system. This dynamic adaptability is particularly important in highly mobile or high-density D2D networks. Traditional game theory often focuses on local equilibrium and ignores the global optimal solution. Chaos-aware games can explore and approximate the global optimal solution by introducing chaotic dynamics, thereby achieving optimal resource allocation at the system level.

[0042] The present invention selects a game strategy through an information perception algorithm. The algorithm has good control capabilities for interference. Compared with similar algorithms, the algorithm has excellent characteristics in convergence speed, solution accuracy, and stability. While ensuring the QoS of users in the system, it can obtain higher system throughput. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of the method of the present invention;

[0044] Figure 2 It is a system model diagram of the present invention;

[0045] Figure 3 It is the result simulation diagram of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Example

[0048] like Figure 1-3 As shown, a D2D communication resource allocation method based on chaos-aware game proposed in one embodiment of the present invention includes the following steps:

[0049] S1: Data input: Initialize user location information, calculate channel gain, signal-to-noise ratio, throughput and other related parameters between users;

[0050] The channel gain is a key factor to be considered in the process of signal transmission and power allocation. The channel gain model in a single-cell communication system is analyzed below. The channel gain between the base station and the cellular user is g kb ; The channel gain between the base station and the D2D user is g ib ; The channel gain between cellular users and D2D users is g ki ; The channel gain between the D2D user transmitter and receiver is g ij Therefore, the signal-to-noise ratio expressions of cellular user i and D2D user j are:

[0051]

[0052] The total system throughput is expressed as:

[0053] The system model is a mode in which multiple D2D users reuse the same cellular user channel resources, referred to as the "many-to-one" mode. The goal is to maximize the total system throughput under various constraints.

[0054] S2: Mathematical modeling: The base station broadcasts each user's information, establishes a chaotic game framework, and calculates each user's utility function;

[0055] The core idea of ​​the chaos game framework is to describe the strategy evolution of the game parties through a chaotic system, thereby capturing the complexity and uncertainty in the system. The chaos game framework emphasizes the system's sensitivity to initial conditions, the so-called "butterfly effect". In this framework, even if users adopt the same strategy, slight differences in initial states may lead to completely different game results. The chaos game framework also takes into account the existence of self-organization phenomena in the system. In many complex systems, local interactive behaviors can lead to global ordered structures or complex phenomena. This self-organizing behavior may lead to unexpected results in strategy evolution in the game. The innovative framework of chaos game theory is used to deal with D2D communication resource allocation problems, especially when dealing with nonlinear and complex dynamic systems. The resource reuse location of D2D users is optimized by generating multiple candidate solutions within the Sierpinski triangle, where each candidate solution represents a different optimization decision variable matrix A, whose element a i,j Represents the specific location of the D2D user in the search space. The initial location is randomly distributed in a triangular area, and the formula Calculation is performed. After that, the candidate solution is dynamically adjusted by controlling the parameters to ensure the optimal allocation of resources. These adjustment steps reflect the self-organizing behavior of D2D users in dynamic nonlinear systems and the self-similar patterns existing in the system. The chaotic game framework provides a new perspective and tool for solving the complex D2D communication resource allocation problem by combining the self-organization, self-similarity and sensitivity to initial conditions of chaotic systems. In the system, users participate in the game in a competitive manner, each pursuing the maximization of their own utility without considering the impact on other users. Therefore, users will improve their utility by continuously increasing the transmission power. In the end, all users will communicate at the maximum power, resulting in increased co-channel interference, decreased system throughput, and increased energy consumption. This game result obviously cannot achieve the optimal system throughput. In order to suppress vicious competition in non-cooperative games, a utility function can be introduced so that users can pay the price for the negative impact of their behavior while gaining benefits, thereby achieving self-optimization without affecting others, and promoting implicit cooperative behavior in the game to improve the overall benefits of the system. The utility functions of cellular users and D2D users can be expressed as:

[0056]

[0057] When a user causes greater interference to users with the same frequency, the price paid by the user is also greater; however, when a user is interfered with more by users with the same frequency, the price paid by the user also becomes smaller. Therefore, the utility function can suppress vicious competition among users.

[0058] S3: Strategy selection: Select the game strategy based on the information perception algorithm;

[0059] The game strategy is selected according to the information perception algorithm. The information perception algorithm is an improved algorithm based on the heuristic algorithm, which includes a three-stage optimization process: information collection stage, information perception and evaluation stage, and information analysis stage. In the information collection stage, the process of human beings collecting information from various sources is simulated to construct an initial solution set. In the information perception and evaluation stage, the information is perceived and evaluated according to predefined standards, redundant information is eliminated, and information with potential is retained. In the stage of analyzing information, the optimal solution is identified.

[0060] S3.1: Initialize the information system and collect comprehensive information. This process can be described as follows:

[0061]

[0062] S3.2: The mathematical modeling of the information perception algorithm exploration phase is:

[0063]

[0064] rand is a randomly generated number, and Δ is the error caused by subjective factors when filtering and evaluating information, which is defined by the following formula:

[0065]

[0066] Where Ξ is the subjective influence factor, which plays a key role as a quantitative indicator of individual subjectivity. It illustrates the uncertainty inherent in filtering and evaluating information, which means that even if the same information is provided, the system may evaluate different user information or the same user at different time points differently due to changes in subjective states. The calculation formula of the subjective influence factor is as follows:

[0067] Ξ=2×mod(3.468×ν×(1-β)×(acos(γ×10 4 ),1)

[0068] Γ is defined as a reliability factor. The inspiration for constructing its mathematical model comes from the ability of humans to dynamically adjust the filtering and evaluation process when processing information. As the processing progresses, the information gradually changes from complex to simple, and the mathematical model is gradually simplified; therefore, Γ reflects the ability of the algorithm to optimize its behavior by self-adjusting according to the quality of information at different iteration stages. This design greatly enhances the adaptability and flexibility of the algorithm and effectively improves the reliability and accuracy of the equation calculation results. The calculation formula of the reliability factor is as follows:

[0069]

[0070] The exponential processing of the sine function provides a dynamic adjustment mechanism for the algorithm. In the initial stage, the changes are significant, and the screening strategy can be adjusted quickly. In the later stage, the rate of change gradually slows down, which is conducive to the stability and fine-tuning of the algorithm. The logarithmic part ensures the stability of the iterative process and smoothes the impact as the number of iterations increases, simulating the transition process of information processing from coarse screening to fine screening. The information quality factor Φ ensures the key role of information quality in affecting Γ, preventing the algorithm from ignoring the basic requirements for information quality when over-optimizing the iterative dynamics.

[0071] S3.3: Information analysis phase: The algorithm is dynamically optimized based on the quality and reliability of the information, thereby significantly improving the accuracy and effectiveness of the final information. This adaptive adjustment mechanism enables the algorithm to conduct deeper information mining and broader information integration. In the next iteration, the new information subject will completely replace the old information subject.

[0072] S4: Iterative update: Iterate the cycle to reach Nash equilibrium;

[0073] The iterative cycle to reach Nash equilibrium includes the following steps:

[0074] S4.1: In a non-cooperative game, for each player, the strategy space is a non-empty and closed convex set, the utility function is a continuous function in the strategy space, and the utility function is a quasi-concave function, then the non-cooperative game has at least one Nash equilibrium solution;

[0075] S4.2: Determine the best response function based on the chaotic game framework and information perception algorithm. The utility function describes the benefits that participants can obtain from different strategy choices in the game, while the best response function is to find the strategy choice that maximizes the utility function given the strategies of other participants. The utility function is the basis for participants to optimize their strategies, and the best response function reflects the results of this optimization.

[0076] S4.3: Prove the positivity, monotonicity, and scalability of the best response function to determine that the Nash equilibrium solution of the non-cooperative game model is unique, and the Nash equilibrium solution is the optimal solution for power allocation and channel selection for each user.

[0077] S5: Data output: Output the channel selection and power allocation results of each user.

[0078] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A D2D communication resource allocation method based on chaos perception game, characterized by: The following steps are involved: S1: Data input: Initialize user location information, calculate channel gain, signal-to-noise ratio, throughput and other related parameters between users; S2: Mathematical modeling: The base station broadcasts each user's information, establishes a chaotic game framework, and calculates each user's utility function; S3: Strategy selection: Select the game strategy based on the information perception algorithm; S4: Iterative update: Iterate the cycle to reach Nash equilibrium; S5: Data output: Output the channel selection and power allocation results of each user.

2. According to claim 1, a D2D communication resource allocation method based on chaos perception game is characterized in that: The channel gain in S1 is a key factor to be considered in the signal transmission and power allocation process. The channel gain model in the communication system is analyzed below; The channel gain between the base station and the cellular user is g kb ; The channel gain between the base station and the D2D user is g ib ; The channel gain between cellular users and D2D users is g ki ; The channel gain between the D2D user transmitter and receiver is g ij ; Therefore, the signal-to-noise ratio expressions of cellular user i and D2D user j are: The total system throughput is expressed as: The system model is a mode in which multiple D2D users reuse the same cellular user channel resources, referred to as the "many-to-one" mode, and the goal is to maximize the total system throughput under various constraints.

3. The D2D communication resource allocation method based on chaos-aware game according to claim 2, characterized in that: The chaotic game framework is established in S2, and the utility function of each user is calculated. The resource reuse position of the D2D user is optimized by generating multiple candidate solutions in the Sierpinski triangle, where each candidate solution represents a different optimization decision variable matrix, and its element a i,j Represents the specific location of the D2D user in the search space; the initial location is randomly distributed in a triangular area, and the formula Calculation is performed; afterwards, the candidate solution is dynamically adjusted through control parameters to ensure the optimal allocation of resources. These adjustment steps reflect the self-organizing behavior of D2D users in dynamic nonlinear systems and the self-similar patterns existing in the system. In order to suppress the vicious competition in non-cooperative games, a utility function can be introduced so that users can pay the price for the negative impact of their behavior while gaining benefits, thereby achieving self-optimization without affecting others, and promoting implicit cooperative behavior in the game to improve the overall efficiency of the system. The utility functions of cellular users and D2D users can be expressed as: When a user causes greater interference to users with the same frequency, the price paid by the user is also greater; however, when a user is interfered with more by users with the same frequency, the price paid by the user also becomes smaller. Therefore, the utility function can suppress vicious competition among users.

4. The method for allocating D2D communication resources based on chaos-aware game according to claim 3, characterized in that: In S3, a game strategy is selected according to an information perception algorithm. The information perception algorithm is an improved algorithm based on a heuristic algorithm and includes a three-stage optimization process: an information collection stage, an information perception and evaluation stage, and an information analysis stage. In the information collection phase, the process of human beings collecting information from various sources is simulated to construct an initial solution set. In the information perception and evaluation phase, the information is perceived and evaluated according to predefined criteria, redundant information is eliminated, and information with potential is retained. In the information analysis phase, the optimal solution is identified. S3.1: Initialize the information system and collect comprehensive information. This process can be described as follows: S3.2: The mathematical modeling of the information perception algorithm exploration phase is: rand is a randomly generated number, and Δ is the error caused by subjective factors when filtering and evaluating information, which is defined by the following formula: Where Ξ is the subjective influence factor, which plays a key role as a quantitative indicator of individual subjectivity. It illustrates the uncertainty inherent in filtering and evaluating information, which means that even if the same information is provided, the system may evaluate different user information or the same user at different time points differently due to changes in subjective states. The calculation formula of the subjective influence factor is as follows: Ξ=2×mod(3.468×ν×(1-β)×(acos(γ×10 4 ),1) Γ is defined as a reliability factor. The inspiration for constructing its mathematical model comes from the ability of humans to dynamically adjust the filtering and evaluation process when processing information. As the processing progresses, the information gradually changes from complex to simple, and the mathematical model is gradually simplified; therefore, Γ reflects the ability of the algorithm to optimize its behavior by self-adjusting according to the quality of information at different iteration stages. This design greatly enhances the adaptability and flexibility of the algorithm and effectively improves the reliability and accuracy of the equation calculation results. The calculation formula of the reliability factor is as follows: The exponential processing of the sine function provides a dynamic adjustment mechanism for the algorithm. In the initial stage, the changes are significant, and the screening strategy can be adjusted quickly. In the later stage, the rate of change gradually slows down, which is conducive to the stability and fine-tuning of the algorithm. The logarithmic part ensures the stability of the iterative process and smoothes the impact as the number of iterations increases, simulating the transition process of information processing from coarse screening to fine screening. The information quality factor Φ ensures the key role of information quality in affecting Γ, preventing the algorithm from ignoring the basic requirements for information quality when over-optimizing the iterative dynamics. S3.3: Information analysis phase: The algorithm is dynamically optimized based on the quality and reliability of the information, thereby significantly improving the accuracy and effectiveness of the final information. This adaptive adjustment mechanism enables the algorithm to conduct deeper information mining and broader information integration. In the next iteration, the new information subject will completely replace the old information subject.

5. The method for allocating D2D communication resources based on chaos-aware game according to claim 4, characterized in that: The iterative loop in S4 reaches the Nash equilibrium including the following steps: S4.1: In a non-cooperative game, for each player, the strategy space is a non-empty and closed convex set, the utility function is a continuous function in the strategy space, and the utility function is a quasi-concave function, then the non-cooperative game has at least one Nash equilibrium solution; S4.2: Determine the best response function based on the chaotic game framework and information perception algorithm. The utility function describes the benefits that participants can obtain from different strategy choices in the game, while the best response function is to find the strategy choice that maximizes the utility function given the strategies of other participants. The utility function is the basis for participants to optimize their strategies, and the best response function reflects the results of this optimization. S4.3: Prove the positivity, monotonicity, and scalability of the best response function to determine that the Nash equilibrium solution of the non-cooperative game model is unique, and the Nash equilibrium solution is the optimal solution for power allocation and channel selection for each user.

Images