Anti-interference robust spectrum access control method and system based on fuzzy game
By employing fuzzy learning algorithms based on fuzzy game theory and co-evolution, the problem of dynamic uncertainty in channel state during multi-channel power control was solved, enabling a more efficient anti-interference strategy and improving the robustness and stability of the communication system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-26
- Publication Date
- 2026-03-27
AI Technical Summary
Existing technologies cannot effectively address the dynamic uncertainty of channel states in multi-channel power control, resulting in insufficient real-time performance and robustness of anti-interference strategies. Furthermore, they rely on complex statistical models and data quality, making them susceptible to adversarial attacks.
A robust spectrum access control method based on fuzzy game theory is adopted. The channel gain and utility are represented by fuzzy numbers, a fuzzy generalized lottery Broto game model is constructed, and the fuzzy Nash equilibrium is solved by a co-evolutionary fuzzy learning algorithm to achieve dynamic optimization of power allocation between users and interferers.
The system's robustness and control efficiency were improved, its dependence on complex models and data quality was reduced, and its adaptability to dynamic environments was enhanced. Simulation results show that the communication quality was significantly improved.
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Figure CN121751376A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electromagnetic spectrum technology, and in particular to an anti-interference robust spectrum access control method and system based on fuzzy game theory. Background Technology
[0002] Power control, as a backup measure for anti-interference spectrum access, can ensure that the signal reaches the receiver without wasting energy by precisely adjusting the transmission power, thereby improving the quality and efficiency of communication.
[0003] Extensive research has been conducted in academia and industry on the problem of power control and anti-interference, but most of these studies have focused on single-channel environments, limiting their applicability. While many works have explored multi-channel power control under adversarial conditions using colonel games (e.g., 1. N. Namvar, W. Saad, N. Bahadori and B. Kelley, “Jamming in the Internet of Things: A Game-Theoretic Perspective,” 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA, 2016, pp.1-6; 2. L. Zhang et al., “Anti-Jamming Colonel Blotto Game for UnderwaterAcoustic Backscatter Communication,” IEEE Trans. Vehicular Technol., vol. 73, no. 7, pp. 10181-10195, July 2024.), these works rely on idealized assumptions about the channel state and require unbiased perception of channel state information by the user. The real-time dynamic uncertainty of channel state information has a significant impact on communication quality. Therefore, current research on multi-channel power control under adversarial conditions is, in effect, a static optimization method.
[0004] To address the difficulties in solving the anti-interference problem caused by the dynamic uncertainty of channel states, the most common solutions are as follows: 1) Machine learning and model prediction methods (such as H. Wang, Q. Wang and Q. Chen, “Opponent's Dynamic Prediction Model-Based Power Control Scheme in SecureTransmission and Smart Jamming Game,” IEEE Internet of Things Journal, vol.11, no. 2, pp. 2870-2882, 15 Jan.15, 2024.), which explore the environment in an unknown environment through continuous trial and error, and then continuously adjust the agent's decisions to eventually converge to the optimal strategy. 2) Robust control methods (such as Y. Gao, Y. Wu, Z. Cui, H. Chen and W. Yang, “Robust Design for Turning and Climbing Angle-Constrained UAV Communication Under Malicious Jamming,” IEEE Communications Letters, vol. 25, no. 2, pp. 584-588, Feb. 2021.) combine modeling optimization, stability analysis (Lyapunov method), and online adaptation (parameter estimation and compensation) to achieve effective control of time-varying channels. 3) Meta-game theory and meta-learning methods (such as Q. Chen, Y. Niu, B. Wan, and P. Xiang, “A Novel Intelligent Anti-Jamming Algorithm Based on Deep Reinforcement Learning Assisted by Meta-Learning for Wireless Communication Systems” Applied Sciences, vol. 13, no. 23, 12642, Feb. 2023.), the core of which is to model channel dynamics and information loss as “meta-tasks”, and to achieve rapid self-optimization of communication systems in complex environments by learning general adaptive strategies from multiple tasks.
[0005] While the methods described above can address the high dynamics and uncertainty of interference environments to some extent, they still have the following drawbacks: 1) Machine learning and meta-learning rely on data quality, and robust control relies on model accuracy; these methods have high requirements for data and models. 2) They have high real-time requirements and complexity, resulting in significant computational overhead. 3) Machine learning and meta-learning are highly sensitive to data and are susceptible to adversarial attacks. Summary of the Invention
[0006] In order to solve the problems existing in the prior art, the present invention provides the following technical solution.
[0007] The first aspect of this invention provides an anti-interference robust spectrum access control method based on fuzzy game theory, comprising: In wireless communication, the channel gain from the user and the interferer to the receiver is obtained, and the channel gain is expressed in fuzzy number to obtain the fuzzy channel gain of the user and the interferer respectively. Based on the fuzzy channel gain of the user and the interferer, determine the fuzzy utility of the user and the interferer respectively; Constructing a fuzzy generalized Lotto Broto game model; By using a fuzzy learning algorithm based on co-evolution, the fuzzy Nash equilibrium of the fuzzy generalized lottery Broto game model is solved, and a power allocation scheme that maximizes the fuzzy utility of both the user and the interferer is obtained.
[0008] Preferably, determining the fuzzy utility of each user and the interferer includes: The fuzzy signal-to-interference-plus-noise ratio is calculated based on the fuzzy channel gain of both the user and the interferer. The probability that the fuzzy signal-to-interference-plus-noise ratio (SNR) is greater than or equal to the threshold is taken as the user's fuzzy utility; the probability that the fuzzy SNR is less than the threshold is taken as the interferer's fuzzy utility.
[0009] Preferably, the fuzzy signal-to-interference-plus-noise ratio is calculated using the following method: in, For the user's fuzzy channel gain, These represent the left and right boundaries of the user's fuzzy channel gain, respectively. The peak value of the user's fuzzy channel gain; For the ambiguous channel gain of the interferer, These represent the left and right boundaries of the interfering party's fuzzy channel gain. The peak value of the interfering party's obfuscated channel gain; and These represent the power of the user and the interferer on the channel set, respectively. For channel The signal-to-interference-plus-noise ratio (SIR) is blurred. It is a constant.
[0010] Preferably, the fuzzy generalized Lotto Broto game model is expressed as: in, and Users and interferers in the channel set respectively Power on; and The fuzzy utility of the user and the interferer, respectively.
[0011] Preferably, the step of using a fuzzy learning algorithm based on co-evolution to solve the fuzzy Nash equilibrium of the fuzzy generalized Lotto Broto game model includes: Calculate the expected payoff for individuals in the user population and the expected payoff for individuals in the interference population, respectively. For any individual in the user population and the interference population, randomly select another individual from the same population for a comparison of expected payoffs. If the individual's payoff is less than that of the other individual, then replicate the other individual's power allocation strategy. The random number generated within the interval and the preset mutation rate are used. If the random number is less than the mutation rate, then any two channels in the channel set are randomly selected, and the power of the first channel is... Added to the second channel, where, or , and These represent the power of the user and the interferer on the channel set, respectively. and The number of power shares equally allocated to users and interferers; iterating through all individuals; The process continues until the set number of iterations is reached, at which point a fuzzy Nash equilibrium is obtained.
[0012] A second aspect of the present invention provides an anti-interference robust spectrum access control system based on fuzzy game theory, comprising: The fuzzy representation module is used to obtain the channel gain from the user and the interferer to the receiver in wireless communication, and to represent the channel gain with fuzzy numbers to obtain the fuzzy channel gain of the user and the interferer respectively. The fuzzy utility determination module is used to determine the fuzzy utility of the user and the interferer based on their respective fuzzy channel gains. The game model building module is used to construct fuzzy generalized Lotto Broto game models; The solution module is used to solve the fuzzy Nash equilibrium of the fuzzy generalized lottery Broto game model using a fuzzy learning algorithm based on co-evolution, and obtain the power allocation scheme that maximizes the fuzzy utility of both the user and the interferer.
[0013] Preferably, the fuzzy utility determination module uses the following method to determine the fuzzy utility of the user and the interferer respectively: The fuzzy signal-to-interference-plus-noise ratio is calculated based on the fuzzy channel gain of both the user and the interferer. The probability that the fuzzy signal-to-interference-plus-noise ratio (SNR) is greater than or equal to the threshold is taken as the user's fuzzy utility; the probability that the fuzzy SNR is less than the threshold is taken as the interferer's fuzzy utility.
[0014] Preferably, the fuzzy utility determination module calculates the fuzzy signal-to-interference-plus-noise ratio using the following method: in, For the user's fuzzy channel gain, These represent the left and right boundaries of the user's fuzzy channel gain, respectively. The peak value of the user's fuzzy channel gain; For the ambiguous channel gain of the interferer, These represent the left and right boundaries of the interfering party's fuzzy channel gain. The peak value of the interfering party's obfuscated channel gain; and These represent the power of the user and the interferer on the channel set, respectively. For channel The signal-to-interference-plus-noise ratio (SIR) is blurred. It is a constant.
[0015] Preferably, the game model construction module uses the following formula to represent the fuzzy generalized Lotto Broto game model: in, and Users and interferers in the channel set respectively Power on; and The fuzzy utility of the user and the interferer, respectively.
[0016] Preferably, the solution module uses a fuzzy learning algorithm based on co-evolution to solve the fuzzy Nash equilibrium of the fuzzy generalized Lotto Broto game model: Calculate the expected payoff for individuals in the user population and the expected payoff for individuals in the interference population, respectively. For any individual in the user population and the interference population, randomly select another individual from the same population for a comparison of expected payoffs. If the individual's payoff is less than that of the other individual, then replicate the other individual's power allocation strategy. The random number generated within the interval and the preset mutation rate are used. If the random number is less than the mutation rate, then any two channels in the channel set are randomly selected, and the power of the first channel is... Added to the second channel, where, or , and These represent the power of the user and the interferer on the channel set, respectively. and The number of power shares equally allocated to users and interferers; iterating through all individuals; The process continues until the set number of iterations is reached, at which point a fuzzy Nash equilibrium is obtained.
[0017] The beneficial effects of this invention are as follows: The anti-interference robust spectrum access control method and system based on fuzzy game theory provided by this invention, through the fuzzy representation of incomplete information, the dynamic system is modeled as an anti-interference fuzzy generalized lottery Broto game model. The fuzzy generalized lottery Broto game model is rescued by fuzzy learning algorithm, and fuzzy Nash equilibrium is obtained. It solves the problem that traditional methods cannot cope with dynamic uncertain environments. Simulation results show the effectiveness of the solution provided by this invention. Compared with related technologies, the significant advantages of this invention are: (1) Unlike existing machine learning, robust control, meta-game and meta-learning methods, this invention models the uncertainty of channel state information and the uncertainty of the resulting payoff using fuzzy numbers. It does not rely on complex statistical models and provides a more intuitive method for dealing with the dynamic uncertainty of channel state information. (2) It provides a fuzzy generalized lottery Broto game method for modeling the multi-channel power control problem between users and interference. This framework provides a perspective for solving related problems from the perspective of fuzzy mathematics. (3) It proposes a fuzzy learning algorithm based on co-evolution, which solves the problem of quickly obtaining the equilibrium solution of the corresponding fuzzy game model. This method enhances system robustness and improves control efficiency. Attached Figure Description
[0018] Figure 1 This is a flowchart illustrating the anti-interference robust spectrum access control method based on fuzzy game theory as described in this invention. Figure 2 This diagram illustrates the convergence process and performance of a fuzzy learning algorithm based on co-evolution, compared to traditional deterministic learning algorithms and random selection learning algorithms. Figure 3 This diagram illustrates the impact of fluctuations in log-normal shadowing on the learning algorithm. Figure 4 This is a functional structure diagram of the anti-interference robust spectrum access control system based on fuzzy game theory described in this invention. Detailed Implementation
[0019] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.
[0020] The method provided by this invention can be implemented in a terminal environment that may include one or more of the following components: a processor, a memory, and a display screen. The memory stores at least one instruction, which is loaded and executed by the processor to implement the method described in the following embodiments.
[0021] A processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the terminal, and performs various functions and processes data by running or executing instructions, programs, code sets or instruction sets stored in memory, and by calling data stored in memory.
[0022] Memory can include random access memory (RAM) or read-only memory (ROM). Memory can be used to store instructions, programs, code, code sets, or instructions.
[0023] The display screen is used to show the user interface of each application.
[0024] In addition, those skilled in the art will understand that the structure of the terminal described above does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include radio frequency circuits, input units, sensors, audio circuits, power supplies, and other components, which will not be described in detail here.
[0025] Example 1 like Figure 1 As shown, this embodiment of the invention provides an anti-interference robust spectrum access control method based on fuzzy game theory, comprising the following steps: S101: Obtain the channel gain from the user and the interferer to the receiver in wireless communication, and represent the channel gain using fuzzy numbers to obtain the fuzzy channel gain for each user and the interferer. In wireless communication, the channel gain from the user and the interferer to the receiver is usually modeled as a random variable to reflect the effects of path loss, shadowing fading, and multipath fading. To handle the uncertainty of channel gain, this invention uses fuzzy mathematics to represent the channel gain as a fuzzy number. That is, the random uncertainty of the channel in wireless communication is transformed into a fuzzy number form, which facilitates subsequent analysis and design within the framework of fuzzy optimization and fuzzy control.
[0026] S102, based on the fuzzy channel gain of both the user and the interferer, determines the fuzzy utility of each. The user and the interferer share the same frequency band, the receiver is the user's receiver, and the interferer causes interference to that receiver. Therefore, the user's utility can be measured by the signal-to-interference-plus-noise ratio (SINR), while the interferer's utility may be the interference it causes to the user.
[0027] S103, construct a fuzzy generalized lottery Broto game model. The game participants include users and interferers. Users aim to maximize their communication utility, while interferers may intend to reduce user utility (malice) or maximize their own utility (selfishness). In the generalized lottery Broto game, each participant allocates a limited resource budget (such as power, time, or frequency resources) across multiple communication channels. Since channel gains are fuzzy, the payoff on each channel is also fuzzy. In classic lottery games, under fuzzy settings, because payoffs are fuzzy numbers, it may be necessary to handle fuzzy probabilities or directly use fuzzy expected payoffs.
[0028] S104 utilizes a fuzzy learning algorithm based on co-evolution to solve the fuzzy Nash equilibrium of the fuzzy generalized lottery Broto game model, thereby obtaining a power allocation scheme that maximizes the fuzzy utility of both the user and the interferer.
[0029] The method provided in this invention considers the dynamic and incomplete channel state information, and the challenge of finding a stable solution arising from this uncertainty. First, fuzzy numbers are used to describe the channel gain and the signal-to-interference-plus-noise ratio (SNR) between the user and the interference, thus modeling the proposed anti-interference problem as a fuzzy generalized lottery bloto game. Further, a fuzzy logic system is used to compare the SNR in fuzzy number form with its threshold, and the output is used as the utility of the game participants, i.e., the probability that the SNR exceeds or falls below its threshold. Finally, to obtain the fuzzy Nash equilibrium (FNE) of the proposed game model, this invention provides a fuzzy learning algorithm based on co-evolution. Simulations verify that this method can overcome the dependence on deterministic information in traditional optimization methods, further improving the robustness of the system.
[0030] In one embodiment of the present invention, the fuzzy utility of the user and the interferer can be determined by the following method: The fuzzy signal-to-interference-plus-noise ratio is calculated based on the fuzzy channel gain of both the user and the interferer. The probability that the fuzzy signal-to-interference-plus-noise ratio (SNR) is greater than or equal to the threshold is taken as the user's fuzzy utility; the probability that the fuzzy SNR is less than the threshold is taken as the interferer's fuzzy utility.
[0031] In one embodiment of the present invention, the fuzzy signal-to-interference-plus-noise ratio can be calculated using the following method: in, For the user's fuzzy channel gain, These represent the left and right boundaries of the user's fuzzy channel gain, respectively. The peak value of the user's fuzzy channel gain; For the ambiguous channel gain of the interferer, These represent the left and right boundaries of the interfering party's fuzzy channel gain. The peak value of the interfering party's obfuscated channel gain; and These represent the power of the user and the interferer on the channel set, respectively. For channel The signal-to-interference-plus-noise ratio (SIR) is blurred. It is a constant.
[0032] In one embodiment of the present invention, the fuzzy generalized Lotto Broto game model can be represented by the following formula: in, and Users and interferers in the channel set respectively Power on; and The fuzzy utility of the user and the interferer, respectively.
[0033] In one embodiment of the present invention, the fuzzy Nash equilibrium of the fuzzy generalized Lotto Broto game model can be solved using a fuzzy learning algorithm based on co-evolution: Calculate the expected payoff for individuals in the user population and the expected payoff for individuals in the interference population, respectively. For any individual in the user population and the interference population, randomly select another individual from the same population for a comparison of expected payoffs. If the individual's payoff is less than that of the other individual, then replicate the other individual's power allocation strategy. The random number generated within the interval and the preset mutation rate are used. If the random number is less than the mutation rate, then any two channels in the channel set are randomly selected, and the power of the first channel is... Added to the second channel, where, or , and These represent the power of the user and the interferer on the channel set, respectively. and The number of power shares equally allocated to users and interferers; iterating through all individuals; The process continues until the set number of iterations is reached, at which point a fuzzy Nash equilibrium is obtained.
[0034] Specifically, users and interference can each be expanded into a population, with each individual in the population subject to its own power budget constraint. The number of users and interference can be represented as follows: and The total number of participants The total power budget for each user and each interference is discretized as follows: and Equal parts; further, set the mutation rate. And each user in the channel set The power allocation across all channels is initialized (randomly allocated). In one example, if 2 watts of power is divided into 10 parts, the available action set is [0, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4, 1.6, 1.8, 2], which are 11 actions. If there are 5 channels, then the total power of 2 watts is randomly allocated across the 5 channels, such as [0.4, 0.4, 0.4, 0.4, 0.4] or [0, 0, 0, 1, 1].
[0035] The expected revenue of an individual in a user population can be calculated using the following formula: The expected payoff of an individual in the disturbed population can be calculated using the following formula: ; These are the power allocation vectors for users and interference across all channels, based on their respective budgets. This represents the power allocation vector for the selected adversarial individuals in the user population during the algorithm's execution. This represents the power allocation vector for the selected adversary individuals in the interference population during algorithm execution. Taking a user as an example: there is only one user, and the strategy is... It must fight against each individual in the interfering population once, and the strategy of the selected individual in the interfering population is... Therefore, different individuals within a population, They are also different.
[0036] For each participant Perform the following procedure: (1) Replication: Randomly select another participant from the same population. Compare expected returns, if ,So copy The power allocation strategy.
[0037] (2) Mutation: In Generate random numbers within the interval ,if Then, randomly select a channel set. Two channels , ,Will On power Add to .in, or .
[0038] After all participants have completed the above replication and mutation process, the loop continues to the step of calculating the individual expected payoff until the set number of iterations is reached, and a fuzzy Nash equilibrium is obtained.
[0039] The fuzzy algorithm provided by this invention is compared with deterministic learning algorithms and random selection learning algorithms in related technologies, and the results are as follows: Figure 2 and Figure 3 As shown. "Crisp learning" is a learning method that relies entirely on immediate user feedback (0 or 1). This method directly determines whether communication is successful based on the current channel state and interference conditions, and adjusts the strategy accordingly. Its feedback results are significantly affected by channel gain fluctuations, leading to obvious volatility during strategy updates. "Random selection" allocates power resources completely randomly in the policy space, without relying on any learning mechanism or historical information. It is a goalless exploratory strategy and is typically used as a baseline method in comparative experiments to highlight the performance advantages of intelligent learning algorithms. The deterministic learning algorithm and the random selection learning algorithm are used as comparison benchmarks in the simulation of this invention to demonstrate the superior stability and convergence of the fuzzy learning algorithm provided by this invention.
[0040] from Figure 2As can be seen, the fuzzy learning algorithm proposed in this invention exhibits significant advantages, with user benefits improved by 20% compared to deterministic learning. Compared to random selection, the improvement is nearly 45%. Traditional deterministic learning algorithms rely on immediate rewards to guide convergence behavior and lack the ability to resist dynamic uncertainties in the environment. Therefore, they only show some improvement in the early stages of the algorithm before the fuzzy logic of the interference is fully trained. However, as the interference strategy is gradually optimized, this small performance improvement obtained by the user disappears.
[0041] from Figure 3 It can be seen that if the user benefit achievable by the fuzzy learning algorithm is considered to be 1, then the best result that can be obtained by using the deterministic learning algorithm or the random selection scheme is 90% of that of the fuzzy learning algorithm. This result further confirms the superior performance of the method proposed in this invention in handling the dynamic incompleteness of channel states.
[0042] Example 2 like Figure 4 As shown, another aspect of the present invention also includes a functional module architecture that is completely consistent with the aforementioned method flow. That is, the embodiments of the present invention also provide an anti-interference robust spectrum access control system based on fuzzy game theory, including: The fuzzy representation module 401 is used to obtain the channel gain from the user and the interferer to the receiver in wireless communication, and to represent the channel gain with fuzzy numbers to obtain the fuzzy channel gain of the user and the interferer respectively. The fuzzy utility determination module 402 is used to determine the fuzzy utility of the user and the interferer based on their respective fuzzy channel gains. Game model building module 403 is used to build a fuzzy generalized Lotto Broto game model; The solver module 404 is used to solve the fuzzy Nash equilibrium of the fuzzy generalized lottery Broto game model using a fuzzy learning algorithm based on co-evolution, and obtain the power allocation scheme that maximizes the fuzzy utility of the user and the interferer.
[0043] Furthermore, the fuzzy utility determination module uses the following method to determine the fuzzy utility of both the user and the interferer: The fuzzy signal-to-interference-plus-noise ratio is calculated based on the fuzzy channel gain of both the user and the interferer. The probability that the fuzzy signal-to-interference-plus-noise ratio (SNR) is greater than or equal to the threshold is taken as the user's fuzzy utility; the probability that the fuzzy SNR is less than the threshold is taken as the interferer's fuzzy utility.
[0044] Furthermore, the fuzzy utility determination module calculates the fuzzy signal-to-interference-plus-noise ratio using the following method: in, For the user's fuzzy channel gain, These represent the left and right boundaries of the user's fuzzy channel gain, respectively. The peak value of the user's fuzzy channel gain; For the ambiguous channel gain of the interferer, These represent the left and right boundaries of the interfering party's fuzzy channel gain. The peak value of the interfering party's obfuscated channel gain; and These represent the power of the user and the interferer on the channel set, respectively. For channel The signal-to-interference-plus-noise ratio (SIR) is blurred. It is a constant.
[0045] Furthermore, the game model construction module uses the following formula to represent the fuzzy generalized Lotto Broto game model: in, and Users and interferers in the channel set respectively Power on; and The fuzzy utility of the user and the interferer, respectively.
[0046] Furthermore, the solution module employs a fuzzy Nash equilibrium of the fuzzy generalized Lotto Broto game model using a fuzzy learning algorithm based on co-evolution: Calculate the expected payoff for individuals in the user population and the expected payoff for individuals in the interference population, respectively. For any individual in the user population and the interference population, randomly select another individual from the same population for a comparison of expected payoffs. If the individual's payoff is less than that of the other individual, then replicate the other individual's power allocation strategy. The random number generated within the interval and the preset mutation rate are used. If the random number is less than the mutation rate, then any two channels in the channel set are randomly selected, and the power of the first channel is... Added to the second channel, where, or , and These represent the power of the user and the interferer on the channel set, respectively. and The number of power shares equally allocated to users and interferers; iterating through all individuals; The process continues until the set number of iterations is reached, at which point a fuzzy Nash equilibrium is obtained.
[0047] The system can be implemented using the anti-interference robust spectrum access control method based on fuzzy game theory provided in Embodiment 1 above. For the specific implementation method, please refer to the description in Embodiment 1, which will not be repeated here.
[0048] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.
Claims
1. A robust spectrum access control method based on fuzzy game theory, characterized in that, include: In wireless communication, the channel gain from the user and the interferer to the receiver is obtained, and the channel gain is expressed in fuzzy number to obtain the fuzzy channel gain of the user and the interferer respectively. Based on the fuzzy channel gain of the user and the interferer, determine the fuzzy utility of the user and the interferer respectively; Constructing a fuzzy generalized Lotto Broto game model; By using a fuzzy learning algorithm based on co-evolution, the fuzzy Nash equilibrium of the fuzzy generalized lottery Broto game model is solved, and a power allocation scheme that maximizes the fuzzy utility of both the user and the interferer is obtained.
2. The anti-interference robust spectrum access control method based on fuzzy game theory as described in claim 1, characterized in that, The determination of the fuzzy utility of each user and the interferer includes: The fuzzy signal-to-interference-plus-noise ratio is calculated based on the fuzzy channel gain of both the user and the interferer. The probability that the fuzzy signal-to-interference-plus-noise ratio (SNR) is greater than or equal to the threshold is taken as the user's fuzzy utility; the probability that the fuzzy SNR is less than the threshold is taken as the interferer's fuzzy utility.
3. The anti-interference robust spectrum access control method based on fuzzy game theory as described in claim 2, characterized in that, The fuzzy signal-to-interference-plus-noise ratio is calculated using the following method: in, For the user's fuzzy channel gain, These represent the left and right boundaries of the user's fuzzy channel gain, respectively. The peak value of the user's fuzzy channel gain; For the ambiguous channel gain of the interferer, These represent the left and right boundaries of the interfering party's fuzzy channel gain. The peak value of the interfering party's obfuscated channel gain; and These represent the power of the user and the interferer on the channel set, respectively. For channel The signal-to-interference-plus-noise ratio (SIR) is blurred. It is a constant.
4. The anti-interference robust spectrum access control method based on fuzzy game theory as described in claim 1, characterized in that, The fuzzy generalized Lotto Broto game model is expressed as follows: in, and Users and interferers in the channel set respectively Power on; and The fuzzy utility of the user and the interferer, respectively.
5. The anti-interference robust spectrum access control method based on fuzzy game theory as described in claim 4, characterized in that, The method of using a fuzzy learning algorithm based on co-evolution to solve the fuzzy Nash equilibrium of the fuzzy generalized Lotto Broto game model includes: Calculate the expected payoff for individuals in the user population and the expected payoff for individuals in the interference population, respectively. For any individual in the user population and the interference population, randomly select another individual from the same population for a comparison of expected payoffs. If the individual's payoff is less than that of the other individual, then replicate the other individual's power allocation strategy. The random number generated within the interval and the preset mutation rate are used. If the random number is less than the mutation rate, then any two channels in the channel set are randomly selected, and the power of the first channel is... Added to the second channel, where, or , and These represent the power of the user and the interferer on the channel set, respectively. and The number of power shares equally allocated to users and interferers; iterating through all individuals; The process continues until the set number of iterations is reached, at which point a fuzzy Nash equilibrium is obtained.
6. A robust spectrum access control system based on fuzzy game theory, characterized in that, include: The fuzzy representation module is used to obtain the channel gain from the user and the interferer to the receiver in wireless communication, and to represent the channel gain with fuzzy numbers to obtain the fuzzy channel gain of the user and the interferer respectively. The fuzzy utility determination module is used to determine the fuzzy utility of the user and the interferer based on their respective fuzzy channel gains. The game model building module is used to construct fuzzy generalized Lotto Broto game models; The solution module is used to solve the fuzzy Nash equilibrium of the fuzzy generalized lottery Broto game model using a fuzzy learning algorithm based on co-evolution, and obtain the power allocation scheme that maximizes the fuzzy utility of both the user and the interferer.
7. The anti-interference robust spectrum access control system based on fuzzy game theory as described in claim 1, characterized in that, The fuzzy utility determination module uses the following method to determine the fuzzy utility of both the user and the interferer: The fuzzy signal-to-interference-plus-noise ratio is calculated based on the fuzzy channel gain of both the user and the interferer. The probability that the fuzzy signal-to-interference-plus-noise ratio is greater than or equal to the threshold is taken as the user's fuzzy utility; The probability that the fuzzy signal-to-interference-plus-noise ratio is less than a threshold is taken as the fuzzy utility of the interfering party.
8. The anti-interference robust spectrum access control system based on fuzzy game theory as described in claim 7, characterized in that, The fuzzy utility determination module calculates the fuzzy signal-to-interference-plus-noise ratio using the following method: in, For the user's fuzzy channel gain, These represent the left and right boundaries of the user's fuzzy channel gain, respectively. The peak value of the user's fuzzy channel gain; For the ambiguous channel gain of the interferer, These represent the left and right boundaries of the interfering party's fuzzy channel gain. The peak value of the interfering party's obfuscated channel gain; and These represent the power of the user and the interferer on the channel set, respectively. For channel The signal-to-interference-plus-noise ratio (SIR) is blurred. It is a constant.
9. The anti-interference robust spectrum access control system based on fuzzy game theory as described in claim 6, characterized in that, The game model construction module uses the following formula to represent the fuzzy generalized Lotto Broto game model: in, and Users and interferers in the channel set respectively Power on; and The fuzzy utility of the user and the interferer, respectively.
10. The anti-interference robust spectrum access control system based on fuzzy game theory as described in claim 9, characterized in that, The solution module employs a fuzzy Nash equilibrium of the fuzzy generalized Lotto Broto game model using a fuzzy learning algorithm based on co-evolution: Calculate the expected payoff for individuals in the user population and the expected payoff for individuals in the interference population, respectively. For any individual in the user population and the interference population, randomly select another individual from the same population for a comparison of expected payoffs. If the individual's payoff is less than that of the other individual, then replicate the other individual's power allocation strategy. The random number generated within the interval and the preset mutation rate are used. If the random number is less than the mutation rate, then any two channels in the channel set are randomly selected, and the power of the first channel is... Added to the second channel, where, or , and These represent the power of the user and the interferer on the channel set, respectively. and The number of power shares equally allocated to users and interferers; iterating through all individuals; The process continues until the set number of iterations is reached, at which point a fuzzy Nash equilibrium is obtained.