Method for improving bandwidth performance of mobile phone antenna through fractal geometric algorithm

Abstract

The invention discloses a method for improving the bandwidth performance of a mobile phone antenna through a fractal geometric algorithm, and the method comprises the following steps: S1, designing an antenna geometric structure, and selecting a self-similar fractal graph; s2, beam forming is carried out to adjust the direction and the gain; s3, sensing the spectrum and selecting an idle frequency band; s4, performing particle swarm optimization on antenna and spectrum allocation; s5, optimizing bandwidth and resisting interference through space-time coding; s6, performing adaptive modulation and coding adjustment; and S7, carrying out spectrum scheduling and bandwidth maximization through an ant colony algorithm. By combining the fractal geometric algorithm, the dynamic beam forming, the spectrum sensing, the resource scheduling and other technologies, the bandwidth performance, the anti-interference capability and the spectrum utilization rate of the mobile phone antenna are optimized, and the overall performance of a communication system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of communication and wireless network, and in particular to a method for improving the bandwidth performance of a mobile phone antenna by using a fractal geometry algorithm. Background Art

[0002] With the rapid development of modern wireless communication technology, the effective use of spectrum resources has become increasingly important. With the widespread application of mobile Internet, Internet of Things (IoT) and the upcoming 5G and 6G communication networks, the demand for wireless spectrum resources has grown exponentially. However, due to the limited spectrum resources and the fixed nature of their use, spectrum congestion and interference problems are becoming increasingly serious, leading to many challenges for existing communication systems. How to effectively improve the bandwidth utilization, anti-interference ability and overall system efficiency of the communication system under limited spectrum resources has become a core issue that needs to be urgently addressed in the current wireless communication field.

[0003] At present, most traditional spectrum allocation mechanisms rely on static spectrum allocation methods. The allocation of spectrum resources is usually based on pre-divided frequency bands or channels, and the spectrum allocation strategy is rigid and lacks flexibility. This static allocation method makes it impossible to dynamically adjust spectrum resources according to actual usage needs, resulting in waste or shortage of spectrum resources. For example, in some frequency bands, spectrum resources are underused during certain time periods, while other frequency bands are overly congested due to high load or interference. Especially in terms of spectrum sensing and spectrum sharing, the existing static spectrum allocation mechanism fails to effectively solve the problems of spectrum sharing and interference management between different communication systems, thus affecting the performance and efficiency of the entire wireless communication network.

[0004] In order to overcome the above problems, dynamic spectrum management and adaptive resource scheduling technologies have gradually become research hotspots in recent years. As an intelligent wireless communication solution, cognitive radio (CR) technology can flexibly and dynamically select and switch idle spectrum and optimize the use of spectrum resources by sensing and analyzing spectrum usage in real time. However, the existing cognitive radio technology still faces some limitations in practical applications, which are mainly reflected in the following aspects: First, traditional spectrum sensing methods usually rely on simple signal strength detection, which cannot accurately identify the idleness of the spectrum and are easily affected by noise and interference, resulting in insufficient accuracy of spectrum sensing; secondly, spectrum sharing and spectrum allocation algorithms still have problems of low efficiency and poor anti-interference ability, and cannot guarantee the spectrum allocation efficiency and service quality in multi-user or multi-system environments.

[0005] In addition, existing antenna technology also has certain limitations in improving the bandwidth utilization and anti-interference capabilities of communication systems. Although multiple-input multiple-output (MIMO) technology can improve the capacity and transmission rate of the system to a certain extent, in practical applications, MIMO systems rely on the configuration and optimization of antenna arrays. If the antenna array is not configured properly, or the phase and gain adjustments between antenna units are not accurate, it may lead to a decrease in signal transmission quality or even signal interference. Therefore, the configuration and optimization of antenna arrays remains a complex challenge in practical systems.

[0006] In order to solve these problems, wireless communication technologies based on artificial intelligence and intelligent optimization algorithms have received widespread attention in recent years. Intelligent optimization algorithms such as particle swarm optimization (PSO) and ant colony algorithm (ACO) are applied to the optimization of spectrum sensing and resource allocation, which can effectively improve the accuracy and efficiency of spectrum allocation. These intelligent algorithms can find the optimal resource allocation scheme in a multi-dimensional and complex search space by simulating the heuristic mechanism of nature. However, these algorithms also have some problems in practical applications. First, in large-scale networks, the computational complexity of particle swarm optimization and ant colony algorithm is high, which easily leads to low computational efficiency. Secondly, the convergence and stability problems of optimization algorithms are still bottlenecks in practical applications, especially in dynamically changing wireless channels and changing network environments. How to ensure that the algorithm can converge quickly and find the global optimal solution is still a challenge.

[0007] As a signal processing technology, space-time coding technology can improve signal reliability, reduce interference, and improve bandwidth efficiency through redundant transmission in the space and time domains. In a multi-antenna system, space-time coding technology can effectively resist channel fading and interference by transmitting information on multiple antennas and in different time intervals, thereby improving system performance. However, the implementation of space-time coding technology often needs to be designed and adjusted according to the specific antenna array configuration. Especially when spectrum resources and antenna resources are limited, how to reasonably allocate spectrum resources and adjust antenna configuration to maximize bandwidth efficiency and anti-interference capabilities is still a complex problem.

[0008] In addition, existing spectrum sensing and resource scheduling algorithms mostly use fixed spectrum allocation strategies, which are difficult to cope with spectrum allocation requirements in dynamic environments. In a multi-user and multi-task communication environment, how to dynamically adjust spectrum resource allocation and antenna array configuration based on real-time spectrum sensing data to maximize bandwidth utilization and anti-interference capabilities is an urgent problem to be solved.

[0009] Therefore, how to provide a method for improving the bandwidth performance of mobile phone antennas through fractal geometry algorithms is an urgent problem that those skilled in the art need to solve. Summary of the invention

[0010] One purpose of the present invention is to propose a method for improving the bandwidth performance of a mobile phone antenna through a fractal geometry algorithm. Through the application of the fractal geometry algorithm, the antenna array of the present invention can achieve higher spectrum utilization efficiency and a wider operating frequency band; secondly, the optimization of the antenna array configuration not only improves the radiation performance of the antenna, but also enhances the multi-band performance and bandwidth efficiency of the system; in addition, combined with spectrum sensing data and ant colony algorithm, the present invention can adjust the antenna array configuration and spectrum resource allocation in real time, and intelligently select the optimal frequency band and resource allocation scheme in a dynamic environment, thereby improving the overall performance and anti-interference capability of the system.

[0011] According to an embodiment of the present invention, a method for improving the bandwidth performance of a mobile phone antenna by using a fractal geometry algorithm comprises the following steps:

[0012] S1. Design the geometric structure of the mobile phone antenna based on the fractal geometry algorithm, select self-similar fractal graphics as antenna elements, embed them into the antenna array, and optimize the performance of the antenna array;

[0013] S2. Using the antenna array, adjusting the beam direction and gain of the antenna array through dynamic beamforming technology to generate a beamforming configuration result;

[0014] S3. Use the beamforming configuration results and cognitive radio technology to perform spectrum sensing, monitor frequency band usage in real time, and select idle or optimal frequency bands for communication;

[0015] S4, using spectrum sensing data, combined with antenna array, and particle swarm optimization algorithm to optimize antenna array and spectrum resource allocation;

[0016] S5, using the optimized antenna array and spectrum resource allocation, applying space-time coding technology, transmitting redundant data through multiple antennas and time intervals, optimizing bandwidth efficiency and anti-interference capability;

[0017] S6, based on optimized bandwidth efficiency and anti-interference capability, dynamically adjusts the modulation mode and coding rate according to channel quality and environmental changes through adaptive modulation and coding strategies;

[0018] S7. Utilize the dynamically adjusted modulation mode and coding rate, combine spectrum sensing data and optimized antenna array, and use the ant colony algorithm to perform spectrum sensing and resource scheduling to maximize bandwidth utilization.

[0019] Optionally, the S1 specifically includes:

[0020] S11. Design the geometric structure of the mobile phone antenna based on the fractal geometry algorithm, select self-similar fractal graphics as antenna elements, embed them into the antenna array, and preliminarily construct the antenna array;

[0021] S12. Optimize the radiation performance of the antenna within the target operating frequency band by adjusting the geometric size and shape of the self-similar fractal figure:

[0022]

[0023] Among them, P opt (n) is the optimized radiation performance, G 0 is the initial gain, L 0 is the initial antenna size, α is the shape factor, n is the number of recursions, and θ is the antenna radiation angle;

[0024] S13. Combine electromagnetic field simulation analysis tools to simulate the initially constructed antenna array, evaluate its radiation characteristics and bandwidth performance in different frequency bands, and adjust the arrangement and parameters of antenna elements according to the simulation results;

[0025] S14. Based on the simulation results, the simulated annealing algorithm is used to optimize the antenna array structure, including the arrangement and spacing of antenna elements in the antenna array:

[0026]

[0027] Among them, r SA is the optimized antenna array layout, r is the current antenna array layout, λ 1 ,λ 2 ,λ 3 is the weight coefficient, G(r) is the gain of the antenna array, BW(r) is the bandwidth of the antenna array, SLL(r) is the sidelobe level, To solve the optimal antenna array layout that minimizes the objective function;

[0028] S15. Based on the optimized antenna array structure, the fractal geometry algorithm is used to optimize the radiation direction and gain distribution of the antenna array:

[0029]

[0030] Among them, r FGA is the radiation direction and gain distribution of the optimized antenna array, G(r SA ) is the antenna gain, SLL(r SA ) is the side lobe level, D(r SA ) is the radiation directivity, λ 4 ,λ 5 ,λ 6 is the weight coefficient;

[0031] S16. Verify the optimized antenna array through field testing and evaluate the multi-band performance, bandwidth efficiency, gain and anti-interference capability of the antenna array.

[0032] Optionally, the S2 specifically includes:

[0033] S21. Determine the basic structure and operating parameters of the antenna array, select a suitable antenna array type, and set the operating frequency band and antenna spacing;

[0034] S22. According to the target communication scenario, design the target parameters of beamforming, including beam width, gain requirement, directivity and sidelobe suppression, and clarify the optimization target of beamforming;

[0035] S23. Based on dynamic beamforming technology, the phase and amplitude of each antenna unit of the antenna array are adjusted in real time. By controlling the input signals of different antenna units, the radiation direction and gain distribution of the antenna array meet the predetermined goals, and the preliminary beamforming results are obtained:

[0036]

[0037] Among them, the weight coefficient W of the antenna unit is dynamically adjusted by the least mean square algorithm. n :

[0038]

[0039] Where E(δ,∈) is the radiation field intensity of the antenna array in the directions δ and ∈, k n is the wave vector of the antenna unit, r n is the position vector of the antenna unit, N is the number of antenna units in the antenna array, n is the antenna unit, j is the imaginary unit, e is the base of the natural logarithm, μ is the learning rate, d n is the desired radiation direction, y n is the actual received signal, x n is the input of the antenna array;

[0040] S24. Based on the preliminary beamforming results, a genetic algorithm is used to further adjust the weighting coefficient of each antenna unit, refine the beam direction and gain distribution of the antenna array, and finally optimize the directivity and sidelobe level of the beam:

[0041]

[0042] Among them, F(w 1 , w 2 , ..., w N ) is the fitness function, E desired (θ n ) is the expected radiation pattern intensity, E actual (θ n ) is the actual radiation pattern intensity, θ n is the radiation direction angle of the antenna array, σ is the penalty coefficient for sidelobe suppression, SLL(w n) is the sidelobe level;

[0043] S25. Evaluate the performance of the beamforming results, analyze the radiation pattern of the antenna array using the minimum variance distortion-free response algorithm, analyze the directivity, gain distribution, and sidelobe level in different frequency bands, and optimize the configuration of the antenna array based on the evaluation results:

[0044]

[0045] Among them, P(θ) is the radiation intensity, d(θ) is the steering vector, R is the covariance matrix, and d H (θ) is the conjugate transpose of the steering vector, and θ is the antenna radiation angle;

[0046] S26. According to the evaluated beamforming result, adjust the antenna array configuration and the parameters of the optimization algorithm, and finally generate the beamforming configuration result.

[0047] Optionally, the S4 specifically includes:

[0048] S41, collect spectrum sensing data, including signal strength, interference level, and frequency band idle status information of each frequency band, and generate real-time data on spectrum usage;

[0049] S42, determining preliminary configuration parameters of the antenna array according to the acquired spectrum sensing data, combined with communication requirements and environmental information;

[0050] S43. Based on the preliminary configuration parameters of the antenna array and the spectrum sensing data, a particle swarm optimization model is constructed, the optimization target is clarified, and the initial position and velocity of the particles are set:

[0051]

[0052] Among them, the particle velocity update includes inertia term, personal optimal solution term and global optimal solution term:

[0053]

[0054] Among them, after each iteration, the current position of the particle is updated as:

[0055]

[0056] Among them, f(X) is the objective function, P signal,i is the signal power of the ith frequency band, P total,i is the total power of the ith frequency band, Utilization i is the bandwidth utilization of the i-th frequency band, X is the optimization parameter vector, is the velocity of the ith particle at the tth iteration, is the inertia weight, c1 and c 2 are individual and global learning factors, r 1 and r 2 is a random number, is the personal best position of the ith particle at the tth iteration, is the position of the ith particle at the tth iteration, is the global best position in the group, is the velocity of the ith particle at the t+1th iteration, is the position of the i-th particle at the t+1th iteration;

[0057] S44, initialize the particle swarm, and calculate the fitness value of each particle according to the spectrum sensing data. The particles represent different antenna array configurations and spectrum resource allocation schemes. The effect of each antenna array configuration and spectrum resource allocation scheme is measured according to the fitness value of each particle:

[0058]

[0059] in, is the fitness value of the i-th particle, α 1 , α 2 , α 3 is the weighting coefficient, is the complex amplitude of the nth antenna element under the i-th particle configuration, is the interference strength of the nth antenna unit under the i-th particle configuration, P total is the total power of the system, N is the total number of antenna units;

[0060] S45. Use a particle swarm optimization algorithm to iteratively update the particles, adjust the movement trajectory of the particles according to the fitness value of the particles, and optimize the antenna array and spectrum resource allocation.

[0061] Optionally, the S5 specifically includes:

[0062] S51, using the optimized antenna array and spectrum resource allocation scheme, designing a corresponding space-time coding scheme and determining a signal coding method;

[0063] S52, designing a space-time coding matrix according to the space-time coding scheme, performing space-time coding on the input data, and performing redundant transmission of the space-time coded signal through multiple antennas and time intervals;

[0064] S53, allocating the space-time coded signal according to the antenna array configuration, allocating the space-time coded signal to the phase and gain control of each antenna unit, and optimizing the transmission of the space-time coded signal in space:

[0065]

[0066] Among them, y opt (t) is the optimized space-time coded signal, y i (t) is the space-time coded signal of the ith antenna element, g i (t) is the gain control of the i-th antenna element, θ i (t) is the phase control of the ith antenna element, M is the number of antenna elements, e j For signal phase control;

[0067] S54, after completing the allocation of the space-time coded signal, further optimize the power, phase and frequency of the space-time coded signal, and optimize the bandwidth efficiency and anti-interference capability:

[0068] Among them, the objective function J is defined power Optimization of space-time coded signal power:

[0069]

[0070] Among them, the optimization objective function J is defined phase Optimization of the phase of the space-time coded signal:

[0071]

[0072] Among them, the optimization objective function J is defined freq Optimization of the frequency of space-time coded signals:

[0073]

[0074] Among them, P i is the power of the ith antenna element, SNR i is the signal-to-noise ratio of the ith antenna element, I i is the interference level of the ith antenna unit, λ is the regularization coefficient, N is the number of antenna units, H i is the channel matrix of the ith antenna element, θ i is the phase adjustment of the ith antenna element, is the complex exponential representation of the ith antenna element, θ j is the phase adjustment of the jth antenna element, f k is the frequency selection of the kth frequency band, M is the number of available frequency bands, is the frequency modulation of the signal;

[0075] S55, monitoring the transmission quality of the space-time coded signal in real time, and adjusting the antenna array and spectrum resource allocation according to the received feedback signal;

[0076] S56. At the receiving end, signal decoding is performed according to the optimized antenna array and spectrum resource allocation, the quality of the decoded signal is evaluated, and the evaluation result is fed back to the system to optimize the parameters.

[0077] Optionally, the S7 specifically includes:

[0078] S71. Based on the dynamically adjusted modulation mode and coding rate, combined with spectrum sensing data, obtain spectrum usage, including idle status, interference level, and signal strength of each frequency band;

[0079] S72. Obtain the radiation direction and gain data of each antenna unit through the optimized antenna array, identify idle frequency bands and frequency bands with low interference in combination with spectrum sensing data, and establish a preliminary spectrum resource allocation plan:

[0080] R n =f(θ n ,G n ,S n ,I n ,F n );

[0081] Among them, the judgment function f(θ n ,G n ,S n ,I n ,F n )for:

[0082]

[0083] Among them, R n Is the frequency band n available in the spectrum resource allocation scheme? n is the radiation direction of antenna unit n, G n is the gain of antenna element n, S n is the signal strength of frequency band n, I n is the interference level in frequency band n, F n is the idle state of frequency band n, S th is the signal strength threshold, I th is the threshold of interference level, F n =1 means frequency band n is idle;

[0084] S73. Based on the spectrum sensing data and the optimized antenna array, define the objective function of the ant colony algorithm and select the appropriate spectrum bandwidth, modulation mode and coding rate:

[0085] F ant =α 1 ·B·log 2 (M)·R c +α 2 ·S n·G n -α 3 I n ;

[0086] Among them, F ant is the objective function of the ant colony algorithm, α 1 ,α 2 ,α 3 is the weight coefficient, B is the spectrum bandwidth, log is the logarithmic function, R c is the coding rate, M is the modulation mode;

[0087] S74. Spectrum sensing and resource scheduling are performed through the ant colony algorithm. Each ant represents a spectrum resource allocation scheme. The ants simulate the pheromone update rules in nature, continuously explore the optimal spectrum resource scheduling scheme in the search space, and evaluate it according to the fitness function:

[0088]

[0089] Among them, p i (t+1) is the spectrum resource allocation plan of the i-th ant at time t+1, P is the search space, η(p i ) is the heuristic information, τ is the pheromone attenuation coefficient, To select the fitness function F ant (p i ) The maximum spectrum resource allocation scheme, p i is the spectrum resource allocation scheme of the i-th ant;

[0090] S75. Through the iterative optimization of the ant colony algorithm, in each round of iteration, the ants adjust the path according to the historical pheromone and the current spectrum sensing results, update the global pheromone, and obtain the optimal spectrum resource allocation plan:

[0091]

[0092] Among them, τ ij (t+1) is the pheromone concentration on the path (i, j) at the t+1th iteration, τ ij (t) is the pheromone concentration on the path (i, j) in the tth iteration, ρ is the pheromone volatility coefficient, Q is the total amount of pheromone released by each ant, The path p is chosen by the kth ant in the tth iteration. i The fitness value of , k is the index variable, and M is the total number of ants;

[0093] S76. Implement spectrum resource scheduling according to the obtained optimal spectrum resource allocation plan, adjust the frequency band in real time, and maximize bandwidth utilization.

[0094] The beneficial effects of the present invention are:

[0095] The present invention achieves significant beneficial effects by optimizing antenna array design using fractal geometry algorithms and combining spectrum sensing with intelligent optimization algorithms to achieve dynamic spectrum resource scheduling. First, by utilizing the self-similarity and recursive characteristics of fractal geometry, we can design an efficient antenna structure that can achieve a wider operating frequency band, higher spectrum utilization efficiency, and better bandwidth efficiency compared to traditional antenna design methods. Self-similar fractal graphics can not only provide more radiation patterns in a limited space, but also enhance the multi-band characteristics of the antenna array, meeting the needs of modern communication systems for multi-band and high-speed communications.

[0096] Secondly, the geometric structure and arrangement of the antenna array are optimized with the help of fractal geometry algorithms, so that the antenna array has higher radiation efficiency and better gain distribution. In the case of tight spectrum resources and frequent environmental changes, through spectrum sensing and dynamic optimization of the antenna array, the antenna configuration can be adjusted in time, so that the system can always maintain the best signal quality and communication stability under different frequency bands and channel conditions. At the same time, the present invention combines intelligent optimization algorithms (such as ant colony algorithm and particle swarm algorithm) to perform spectrum resource scheduling, so that the utilization efficiency of the spectrum is maximized, interference is reduced and the allocation of the spectrum is optimized. Through the application of intelligent algorithms, the system can perceive the spectrum usage status in real time, select idle frequency bands or low-interference frequency bands for communication, thereby effectively improving bandwidth utilization and system capacity.

[0097] In addition, the present invention has also achieved significant improvement in anti-interference capability through space-time coding technology combined with optimized antenna array and spectrum resource allocation scheme. By transmitting redundant data through multiple antennas and time intervals, the anti-interference capability of the system in multipath propagation and high interference environments is effectively improved. The space-time coding scheme not only enhances the reliability of the signal, but also optimizes the bandwidth efficiency, providing a stable and efficient communication solution for complex and dynamic wireless communication environments.

[0098] In summary, the present invention improves the performance of antenna arrays and the scheduling efficiency of spectrum resources by combining fractal geometry algorithm to design antenna arrays, real-time analysis of spectrum sensing data and intelligent optimization algorithms, fully meets the requirements of modern communication systems for high spectrum utilization, high bandwidth efficiency and strong anti-interference capabilities, and has the potential to be widely used in complex communication environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0100] Figure 1A flow chart of a method proposed by the present invention for improving the bandwidth performance of mobile phone antennas through a fractal geometry algorithm;

[0101] Figure 2 This is a schematic diagram of space-time coded signal allocation proposed by the present invention for improving the bandwidth performance of mobile phone antennas through a fractal geometry algorithm. DETAILED DESCRIPTION

[0102] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0103] refer to Figure 1-2 , a method for improving the bandwidth performance of mobile phone antennas by using a fractal geometry algorithm, comprising the following steps:

[0104] S1. Design the geometric structure of the mobile phone antenna based on the fractal geometry algorithm, select self-similar fractal graphics as antenna elements, embed them into the antenna array, and optimize the performance of the antenna array;

[0105] S2. Using the antenna array, adjusting the beam direction and gain of the antenna array through dynamic beamforming technology to generate a beamforming configuration result;

[0106] S3. Use the beamforming configuration results and cognitive radio technology to perform spectrum sensing, monitor frequency band usage in real time, and select idle or optimal frequency bands for communication;

[0107] S4, using spectrum sensing data, combined with antenna array, and particle swarm optimization algorithm to optimize antenna array and spectrum resource allocation;

[0108] S5, using the optimized antenna array and spectrum resource allocation, applying space-time coding technology, transmitting redundant data through multiple antennas and time intervals, optimizing bandwidth efficiency and anti-interference capability;

[0109] S6, based on optimized bandwidth efficiency and anti-interference capability, dynamically adjusts the modulation mode and coding rate according to channel quality and environmental changes through adaptive modulation and coding strategies;

[0110] S7. Utilize the dynamically adjusted modulation mode and coding rate, combine spectrum sensing data and optimized antenna array, and use the ant colony algorithm to perform spectrum sensing and resource scheduling to maximize bandwidth utilization.

[0111] In this implementation, S1 specifically includes:

[0112] S11. Design the geometric structure of the mobile phone antenna based on the fractal geometry algorithm, select self-similar fractal graphics as antenna elements, embed them into the antenna array, and preliminarily construct the antenna array;

[0113] S12. Optimize the radiation performance of the antenna within the target operating frequency band by adjusting the geometric size and shape of the self-similar fractal figure:

[0114]

[0115] Among them, P opt (n) is the optimized radiation performance, G 0 is the initial gain, L 0 is the initial antenna size, α is the shape factor, n is the number of recursions, and θ is the antenna radiation angle;

[0116] S13. Combine electromagnetic field simulation analysis tools to simulate the initially constructed antenna array, evaluate the radiation characteristics and bandwidth performance in different frequency bands, and adjust the arrangement and parameters of antenna elements according to the simulation results;

[0117] S14. Based on the simulation results, the simulated annealing algorithm is used to optimize the antenna array structure, including the arrangement and spacing of antenna elements in the antenna array:

[0118]

[0119] Among them, r SA is the optimized antenna array layout, r is the current antenna array layout, λ 1 ,λ 2 ,λ 3 is the weight coefficient, G(r) is the gain of the antenna array, BW(r) is the bandwidth of the antenna array, SLL(r) is the sidelobe level, To solve the optimal antenna array layout that minimizes the objective function;

[0120] S15. Based on the optimized antenna array structure, the fractal geometry algorithm is used to optimize the radiation direction and gain distribution of the antenna array:

[0121]

[0122] Among them, r FGA is the radiation direction and gain distribution of the optimized antenna array, G(r SA ) is the antenna gain, SLL(r SA ) is the side lobe level, D(r SA ) is the radiation directivity, λ 4 ,λ 5 ,λ 6 is the weight coefficient;

[0123] S16. Verify the optimized antenna array through field testing and evaluate the multi-band performance, bandwidth efficiency, gain and anti-interference capability of the antenna array.

[0124] In this implementation, S2 specifically includes:

[0125] S21. Determine the basic structure and operating parameters of the antenna array, select a suitable antenna array type, and set the operating frequency band and antenna spacing;

[0126] S22. According to the target communication scenario, design the target parameters of beamforming, including beam width, gain requirement, directivity and sidelobe suppression, and clarify the optimization target of beamforming;

[0127] S23. Based on dynamic beamforming technology, the phase and amplitude of each antenna unit of the antenna array are adjusted in real time. By controlling the input signals of different antenna units, the radiation direction and gain distribution of the antenna array meet the predetermined goals, and the preliminary beamforming results are obtained:

[0128]

[0129] Among them, the weighting coefficient w of the antenna unit is dynamically adjusted by the least mean square algorithm. n :

[0130]

[0131] Where E(δ,∈) is the radiation field intensity of the antenna array in the directions δ and ∈, k n is the wave vector of the antenna unit, r n is the position vector of the antenna unit, N is the number of antenna units in the antenna array, n is the antenna unit, j is the imaginary unit, e is the base of the natural logarithm, μ is the learning rate, d n is the desired radiation direction, y n is the actual received signal, x n is the input of the antenna array;

[0132] S24. Based on the preliminary beamforming results, a genetic algorithm is used to further adjust the weighting coefficient of each antenna unit, refine the beam direction and gain distribution of the antenna array, and finally optimize the directivity and sidelobe level of the beam:

[0133]

[0134] Among them, F(w 1 ,w 2 ,...,w N ) is the fitness function, E desired (θ n ) is the expected radiation pattern intensity, E actual (θ n ) is the actual radiation pattern intensity, θ nis the radiation direction angle of the antenna array, σ is the penalty coefficient for sidelobe suppression, SLL(w n ) is the sidelobe level;

[0135] S25. Evaluate the performance of the beamforming results, analyze the radiation pattern of the antenna array using the minimum variance distortion-free response algorithm, analyze the directivity, gain distribution, and sidelobe level in different frequency bands, and optimize the configuration of the antenna array based on the evaluation results:

[0136]

[0137] Among them, P(θ) is the radiation intensity, d(θ) is the steering vector, R is the covariance matrix, and d H (θ) is the conjugate transpose of the steering vector, and θ is the antenna radiation angle;

[0138] S26. According to the evaluated beamforming result, adjust the antenna array configuration and the parameters of the optimization algorithm, and finally generate the beamforming configuration result.

[0139] In this implementation, S4 specifically includes:

[0140] S41, collect spectrum sensing data, including signal strength, interference level, and frequency band idle status information of each frequency band, and generate real-time data on spectrum usage;

[0141] S42, determining preliminary configuration parameters of the antenna array according to the acquired spectrum sensing data, combined with communication requirements and environmental information;

[0142] S43. Based on the preliminary configuration parameters of the antenna array and the spectrum sensing data, a particle swarm optimization model is constructed, the optimization target is clarified, and the initial position and velocity of the particles are set:

[0143]

[0144] Among them, the particle velocity update includes inertia term, personal optimal solution term and global optimal solution term:

[0145]

[0146] Among them, after each iteration, the current position of the particle is updated as:

[0147]

[0148] Among them, f(X) is the objective function, P signal,i is the signal power of the ith frequency band, P total,i is the total power of the ith frequency band, Utilization i is the bandwidth utilization of the i-th frequency band, X is the optimization parameter vector, is the velocity of the ith particle at the tth iteration, is the inertia weight, c 1 and c 2 are individual and global learning factors, r 1 and r 2 is a random number, is the personal best position of the ith particle at the tth iteration, is the position of the ith particle at the tth iteration, is the global best position in the group, is the velocity of the ith particle at the t+1th iteration, is the position of the i-th particle at the t+1th iteration;

[0149] S44, initialize the particle swarm, and calculate the fitness value of each particle according to the spectrum sensing data. The particles represent different antenna array configurations and spectrum resource allocation schemes. The effect of each antenna array configuration and spectrum resource allocation scheme is measured according to the fitness value of each particle:

[0150]

[0151] in, is the fitness value of the i-th particle, α 1 , α 2 , α 3 is the weighting coefficient, is the complex amplitude of the nth antenna element under the i-th particle configuration, is the interference strength of the nth antenna unit under the i-th particle configuration, P total is the total power of the system, N is the total number of antenna units;

[0152] S45. Use a particle swarm optimization algorithm to iteratively update the particles, adjust the movement trajectory of the particles according to the fitness value of the particles, and optimize the antenna array and spectrum resource allocation.

[0153] In this implementation manner, S5 specifically includes:

[0154] S51, using the optimized antenna array and spectrum resource allocation scheme, designing a corresponding space-time coding scheme and determining a signal coding method;

[0155] S52, designing a space-time coding matrix according to the space-time coding scheme, performing space-time coding on the input data, and performing redundant transmission of the space-time coded signal through multiple antennas and time intervals;

[0156] S53, allocating the space-time coded signal according to the antenna array configuration, allocating the space-time coded signal to the phase and gain control of each antenna unit, and optimizing the transmission of the space-time coded signal in space:

[0157]

[0158] Among them, y opt (t) is the optimized space-time coded signal, y i (t) is the space-time coded signal of the ith antenna element, g i (t) is the gain control of the i-th antenna element, θ i (t) is the phase control of the ith antenna element, M is the number of antenna elements, e j For signal phase control;

[0159] S54, after completing the allocation of the space-time coded signal, further optimize the power, phase and frequency of the space-time coded signal, and optimize the bandwidth efficiency and anti-interference capability:

[0160] Among them, the objective function J is defined power Optimization of space-time coded signal power:

[0161]

[0162] Among them, the optimization objective function J is defined phase Optimization of the phase of the space-time coded signal:

[0163]

[0164] Among them, the optimization objective function J is defined freq Optimization of the frequency of space-time coded signals:

[0165]

[0166] Among them, P i is the power of the ith antenna element, SNR i is the signal-to-noise ratio of the ith antenna element, I i is the interference level of the ith antenna unit, λ is the regularization coefficient, N is the number of antenna units, H i is the channel matrix of the ith antenna element, θ i is the phase adjustment of the ith antenna element, is the complex exponential representation of the ith antenna element, θ j is the phase adjustment of the jth antenna element, f k is the frequency selection of the kth frequency band, M is the number of available frequency bands, is the frequency modulation of the signal;

[0167] S55, monitoring the transmission quality of the space-time coded signal in real time, and adjusting the antenna array and spectrum resource allocation according to the received feedback signal;

[0168] S56. At the receiving end, signal decoding is performed according to the optimized antenna array and spectrum resource allocation, the quality of the decoded signal is evaluated, and the evaluation result is fed back to the system to optimize the parameters.

[0169] In this implementation manner, the S7 specifically includes:

[0170] S71. Based on the dynamically adjusted modulation mode and coding rate, combined with spectrum sensing data, obtain spectrum usage, including idle status, interference level, and signal strength of each frequency band;

[0171] S72. Obtain the radiation direction and gain data of each antenna unit through the optimized antenna array, identify idle frequency bands and frequency bands with low interference in combination with spectrum sensing data, and establish a preliminary spectrum resource allocation plan:

[0172] R n =f(θ n ,G n ,S n ,I n ,F n );

[0173] Among them, the judgment function f(θ n ,G n ,S n ,I n ,F n )for:

[0174]

[0175] Among them, R n Is the frequency band n available in the spectrum resource allocation scheme? n is the radiation direction of antenna unit n, G n is the gain of antenna element n, S n is the signal strength of frequency band n, I n is the interference level in frequency band n, F n is the idle state of frequency band n, S th is the signal strength threshold, I th is the threshold of interference level, F n =1 means frequency band n is idle;

[0176] S73. Based on the spectrum sensing data and the optimized antenna array, define the objective function of the ant colony algorithm and select the appropriate spectrum bandwidth, modulation mode and coding rate:

[0177] F ant =α 1 ·B·log 2 (M)·R c +α 2 ·Sn ·G n -α 3 I n ;

[0178] Among them, F ant is the objective function of the ant colony algorithm, α 1 ,α 2 ,α 3 is the weight coefficient, B is the spectrum bandwidth, log is the logarithmic function, R c is the coding rate, M is the modulation mode;

[0179] S74. Spectrum sensing and resource scheduling are performed through the ant colony algorithm. Each ant represents a spectrum resource allocation scheme. The ants simulate the pheromone update rules in nature, continuously explore the optimal spectrum resource scheduling scheme in the search space, and evaluate it according to the fitness function:

[0180]

[0181] Among them, p i (t+1) is the spectrum resource allocation plan of the i-th ant at time t+1, P is the search space, η(p i ) is the heuristic information, τ is the pheromone attenuation coefficient, To select the fitness function F ant (p i ) The maximum spectrum resource allocation scheme, p i is the spectrum resource allocation scheme of the i-th ant;

[0182] S75. Through the iterative optimization of the ant colony algorithm, in each round of iteration, the ants adjust the path according to the historical pheromone and the current spectrum sensing results, update the global pheromone, and obtain the optimal spectrum resource allocation plan:

[0183]

[0184] Among them, τ ij (t+1) is the pheromone concentration on the path (i, j) at the t+1th iteration, τ ij (t) is the pheromone concentration on the path (i, j) in the tth iteration, ρ is the pheromone volatility coefficient, Q is the total amount of pheromone released by each ant, The path p is chosen by the kth ant in the tth iteration. i The fitness value of , k is the index variable, and M is the total number of ants;

[0185] S76. Implement spectrum resource scheduling according to the obtained optimal spectrum resource allocation plan, adjust the frequency band in real time, and maximize bandwidth utilization.

[0186] Embodiment 1:

[0187] In order to verify the feasibility of the present invention in implementation, the present invention is applied to a central area of ​​a city, which is an environment with high-density users and high-traffic data transmission. In this area, multiple mobile communication base stations need to support the connection of hundreds of user devices at the same time. These user devices are involved in various data transmission tasks, including voice calls, video calls, data stream transmission, etc. Due to the presence of a large number of buildings, walls and other electromagnetic interference sources in the urban environment, the spectrum resources are unevenly distributed in space, and the communication quality is greatly affected. Traditional wireless communication systems face insufficient utilization of spectrum resources, spectrum interference problems and poor antenna array design in this environment.

[0188] First, the geometric structure of the antenna array is designed using a fractal geometry algorithm. Through this algorithm, self-similar fractal patterns are selected as antenna elements and these elements are embedded into the antenna array. The self-similarity of fractal patterns enables the antenna array to achieve excellent radiation performance in multiple frequency bands. Especially in multi-band communication environments, the fractal antenna array has good frequency response characteristics and can provide stronger signal coverage and gain distribution in different frequency bands.

[0189] Next, spectrum sensing technology is used to monitor spectrum usage in real time, identify which frequency bands are idle or have low interference, and select appropriate frequency bands for communication. In this embodiment, the spectrum sensing device can provide real-time information on the signal strength, interference level, and idle state of each frequency band, providing a basis for subsequent spectrum resource allocation.

[0190] Based on spectrum sensing, the ant colony algorithm is combined to dynamically allocate spectrum resources. In this process, each "ant" represents a spectrum resource allocation plan. Ants search based on signal quality and interference level, simulating the behavior of ants in nature, and constantly looking for the optimal spectrum resource scheduling plan in the search space. Through this process, the system can maximize bandwidth utilization while reducing signal interference.

[0191] Finally, space-time coding technology is used to transmit the signal redundantly. According to the optimized antenna array configuration, the space-time coded signal is distributed to the phase and gain control of each antenna unit to optimize the transmission of the signal in space. Through space-time coding, the signal is redundantly transmitted through multiple antennas and different time intervals, which effectively improves the system's anti-interference ability and bandwidth utilization efficiency.

[0192] Table 1 Performance comparison between the present invention and the traditional system

[0193] Test items Traditional system (comparison) The present invention Percentage increase Spectrum resource utilization 58％ 85％ 27％ Signal interference level (dB) -7.2 -12.5 Improved by about 74% Bandwidth utilization efficiency 62％ 90％ 28％ Bit Error Rate (BER) 0.040 0.012 70% improvement

[0194] As can be seen from Table 1, the improvement in spectrum resource utilization has increased from 58% to 85%. This improvement shows that the present invention is more efficient in identifying and utilizing idle spectrum bands, and can effectively reduce the waste of spectrum resources. Secondly, the signal interference level is significantly reduced, from -7.2dB to -12.5dB, a reduction of 5.3dB. This shows that the present invention effectively suppresses signal interference, especially in high-interference areas, and can provide more stable communication quality. In terms of bandwidth utilization efficiency, the present invention has increased from 62% to 90%, an increase of 28%, which means that the bandwidth can be allocated more reasonably, and the data transmission rate is improved, especially in a multi-user environment. The performance is more superior. Finally, the bit error rate (BER) dropped from 0.040 to 0.012, an increase of about 70%. This result proves that the present invention can greatly improve the accuracy of data transmission and reduce errors in the communication process. In general, the present invention significantly improves the performance and reliability of wireless communications through more intelligent resource scheduling and effective interference suppression, and can provide more stable and efficient services in complex communication environments.

[0195] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for improving the bandwidth performance of a mobile phone antenna by using a fractal geometry algorithm, characterized in that: The steps include: S1. Design the geometric structure of the mobile phone antenna based on the fractal geometry algorithm, select self-similar fractal graphics as antenna elements, embed them into the antenna array, and optimize the performance of the antenna array; S2. Using the antenna array, adjusting the beam direction and gain of the antenna array through dynamic beamforming technology to generate a beamforming configuration result; S3. Use the beamforming configuration results and cognitive radio technology to perform spectrum sensing, monitor frequency band usage in real time, and select idle or optimal frequency bands for communication; S4, using spectrum sensing data, combined with antenna array, and particle swarm optimization algorithm to optimize antenna array and spectrum resource allocation; S5, using the optimized antenna array and spectrum resource allocation, applying space-time coding technology, transmitting redundant data through multiple antennas and time intervals, optimizing bandwidth efficiency and anti-interference capability; S6, based on optimized bandwidth efficiency and anti-interference capability, dynamically adjusts the modulation mode and coding rate according to channel quality and environmental changes through adaptive modulation and coding strategies; S7. Utilize the dynamically adjusted modulation mode and coding rate, combine spectrum sensing data and optimized antenna array, and use the ant colony algorithm to perform spectrum sensing and resource scheduling to maximize bandwidth utilization.

2. The method for improving the bandwidth performance of a mobile phone antenna by using a fractal geometry algorithm according to claim 1, characterized in that: The S1 specifically includes: S11. Design the geometric structure of the mobile phone antenna based on the fractal geometry algorithm, select self-similar fractal graphics as antenna elements, embed them into the antenna array, and preliminarily construct the antenna array; S12. Optimize the radiation performance of the antenna within the target operating frequency band by adjusting the geometric size and shape of the self-similar fractal figure: Among them, P opt (n) is the optimized radiation performance, G0 is the initial gain, L0 is the initial antenna size, α is the shape factor, n is the number of recursions, and θ is the antenna radiation angle; S13. Combine electromagnetic field simulation analysis tools to simulate the initially constructed antenna array, evaluate the radiation characteristics and bandwidth performance in different frequency bands, and adjust the arrangement and parameters of antenna elements according to the simulation results; S14. Based on the simulation results, the simulated annealing algorithm is used to optimize the antenna array structure, including the arrangement and spacing of antenna elements in the antenna array: Among them, r SA The optimized antenna array layout is shown in Figure 2. r is the current antenna array layout, λ1, λ2, λ3 are weight coefficients, G(r) is the gain of the antenna array, BW(r) is the bandwidth of the antenna array, SLL(r) is the sidelobe level, To solve the optimal antenna array layout that minimizes the objective function; S15. Based on the optimized antenna array structure, the fractal geometry algorithm is used to optimize the radiation direction and gain distribution of the antenna array: Among them, r FGA is the radiation direction and gain distribution of the optimized antenna array, G(r SA ) is the antenna gain, SLL(r SA ) is the side lobe level, D(r SA ) is the radiation directivity, λ4,λ5,λ6 are weight coefficients; S16. Verify the optimized antenna array through field testing and evaluate the multi-band performance, bandwidth efficiency, gain and anti-interference capability of the antenna array.

3. The method for improving the bandwidth performance of a mobile phone antenna by using a fractal geometry algorithm according to claim 1, characterized in that: The S2 specifically includes: S21. Determine the basic structure and operating parameters of the antenna array, select a suitable antenna array type, and set the operating frequency band and antenna spacing; S22. According to the target communication scenario, design the target parameters of beamforming, including beam width, gain requirement, directivity and sidelobe suppression, and clarify the optimization target of beamforming; S23. Based on dynamic beamforming technology, the phase and amplitude of each antenna unit of the antenna array are adjusted in real time. By controlling the input signals of different antenna units, the radiation direction and gain distribution of the antenna array meet the predetermined goals, and the preliminary beamforming results are obtained: Among them, the weighting coefficient w of the antenna unit is dynamically adjusted by the least mean square algorithm. n : Where E(δ,∈) is the radiation field intensity of the antenna array in the directions δ and ∈, k n is the wave vector of the antenna unit, r n is the position vector of the antenna unit, N is the number of antenna units in the antenna array, n is the antenna unit, j is the imaginary unit, e is the base of the natural logarithm, μ is the learning rate, d n is the desired radiation direction, y n is the actual received signal, x n is the input of the antenna array; S24. Based on the preliminary beamforming results, a genetic algorithm is used to further adjust the weighting coefficient of each antenna unit, refine the beam direction and gain distribution of the antenna array, and finally optimize the directivity and sidelobe level of the beam: Among them, F(w1,w2,...,w N ) is the fitness function, E desired (θ n ) is the expected radiation pattern intensity, E actual (θ n ) is the actual radiation pattern intensity, θ n is the radiation direction angle of the antenna array, σ is the penalty coefficient for sidelobe suppression, SLL(w n ) is the sidelobe level; S25. Evaluate the performance of the beamforming results, analyze the radiation pattern of the antenna array using the minimum variance distortion-free response algorithm, analyze the directivity, gain distribution, and sidelobe level in different frequency bands, and optimize the configuration of the antenna array based on the evaluation results: Among them, P(θ) is the radiation intensity, d(θ) is the steering vector, R is the covariance matrix, and d H (θ) is the conjugate transpose of the steering vector, and θ is the antenna radiation angle; S26. According to the evaluated beamforming result, adjust the antenna array configuration and the parameters of the optimization algorithm, and finally generate the beamforming configuration result.

4. The method for improving the bandwidth performance of a mobile phone antenna by using a fractal geometry algorithm according to claim 1, characterized in that: The S4 specifically includes: S41, collect spectrum sensing data, including signal strength, interference level, and frequency band idle status information of each frequency band, and generate real-time data on spectrum usage; S42, determining preliminary configuration parameters of the antenna array according to the acquired spectrum sensing data, combined with communication requirements and environmental information; S43. Based on the preliminary configuration parameters of the antenna array and the spectrum sensing data, a particle swarm optimization model is constructed, the optimization target is clarified, and the initial position and velocity of the particles are set: Among them, the particle velocity update includes inertia term, personal optimal solution term and global optimal solution term: Among them, after each iteration, the current position of the particle is updated as: Among them, f(X) is the objective function, P signal,i is the signal power of the ith frequency band, P total,i is the total power of the ith frequency band, Utilization i is the bandwidth utilization of the i-th frequency band, X is the optimization parameter vector, is the velocity of the ith particle at the tth iteration, is the inertia weight, c1 and c2 are the individual and global learning factors, r1 and r2 are random numbers, is the personal best position of the ith particle at the tth iteration, is the position of the ith particle at the tth iteration, is the global best position in the group, is the velocity of the ith particle at the t+1th iteration, is the position of the i-th particle at the t+1th iteration; S44, initialize the particle swarm, and calculate the fitness value of each particle according to the spectrum sensing data. The particles represent different antenna array configurations and spectrum resource allocation schemes. The effect of each antenna array configuration and spectrum resource allocation scheme is measured according to the fitness value of each particle: in, is the fitness value of the ith particle, α1, α2, α3 are weighted coefficients, is the complex amplitude of the nth antenna element under the i-th particle configuration, is the interference strength of the nth antenna unit under the i-th particle configuration, P total is the total power of the system, N is the total number of antenna units; S45. Use a particle swarm optimization algorithm to iteratively update the particles, adjust the movement trajectory of the particles according to the fitness value of the particles, and optimize the antenna array and spectrum resource allocation.

5. The method for improving the bandwidth performance of a mobile phone antenna by using a fractal geometry algorithm according to claim 1, characterized in that: The S5 specifically includes: S51, using the optimized antenna array and spectrum resource allocation scheme, designing a corresponding space-time coding scheme and determining a signal coding method; S52, designing a space-time coding matrix according to the space-time coding scheme, performing space-time coding on the input data, and performing redundant transmission of the space-time coded signal through multiple antennas and time intervals; S53, allocating the space-time coded signal according to the antenna array configuration, allocating the space-time coded signal to the phase and gain control of each antenna unit, and optimizing the transmission of the space-time coded signal in space: Among them, y opt (t) is the optimized space-time coded signal, y i (t) is the space-time coded signal of the ith antenna element, g i (t) is the gain control of the i-th antenna element, θ i (t) is the phase control of the ith antenna element, M is the number of antenna elements, e j For signal phase control; S54, after completing the allocation of the space-time coded signal, further optimize the power, phase and frequency of the space-time coded signal, and optimize the bandwidth efficiency and anti-interference capability: Among them, the objective function J is defined power Optimization of space-time coded signal power: Among them, the optimization objective function J is defined phase Optimization of the phase of the space-time coded signal: Among them, the optimization objective function J is defined freq Optimization of the frequency of space-time coded signals: Among them, P i is the power of the ith antenna element, SNR i is the signal-to-noise ratio of the ith antenna element, I i is the interference level of the ith antenna unit, λ is the regularization coefficient, N is the number of antenna units, H i is the channel matrix of the ith antenna element, θ i is the phase adjustment of the ith antenna element, is the complex exponential representation of the ith antenna element, θ j is the phase adjustment of the jth antenna element, f k is the frequency selection of the kth frequency band, M is the number of available frequency bands, is the frequency modulation of the signal; S55, monitoring the transmission quality of the space-time coded signal in real time, and adjusting the antenna array and spectrum resource allocation according to the received feedback signal; S56. At the receiving end, signal decoding is performed according to the optimized antenna array and spectrum resource allocation, the quality of the decoded signal is evaluated, and the evaluation result is fed back to the system to optimize the parameters.

6. The method for improving the bandwidth performance of a mobile phone antenna by using a fractal geometry algorithm according to claim 1, characterized in that: The S7 specifically includes: S71. Based on the dynamically adjusted modulation mode and coding rate, combined with spectrum sensing data, obtain spectrum usage, including idle status, interference level, and signal strength of each frequency band; S72. Obtain the radiation direction and gain data of each antenna unit through the optimized antenna array, identify idle frequency bands and frequency bands with low interference in combination with spectrum sensing data, and establish a preliminary spectrum resource allocation plan: R n =f(θ n ,G n ,S n ,I n ,F n ); Among them, the judgment function f(θ n ,G n ,S n ,I n ,F n )for: Among them, R n Is the frequency band n available in the spectrum resource allocation scheme? n is the radiation direction of antenna unit n, G n is the gain of antenna element n, S n is the signal strength of frequency band n, I n is the interference level in frequency band n, F n is the idle state of frequency band n, S th is the signal strength threshold, I th is the threshold of interference level, F n =1 means frequency band n is idle; S73. Based on the spectrum sensing data and the optimized antenna array, define the objective function of the ant colony algorithm and select the appropriate spectrum bandwidth, modulation mode and coding rate: F ant =α1·B·log2(M)·R c +α2·S n ·G n -α3·I n ; Among them, F ant is the objective function of the ant colony algorithm, α1, α2, α3 are weight coefficients, B is the spectrum bandwidth, log is the logarithmic function, R c is the coding rate, M is the modulation mode; S74. Spectrum sensing and resource scheduling are performed through the ant colony algorithm. Each ant represents a spectrum resource allocation scheme. The ants simulate the pheromone update rules in nature, continuously explore the optimal spectrum resource scheduling scheme in the search space, and evaluate it according to the fitness function: Among them, p i (t+1) is the spectrum resource allocation plan of the i-th ant at time t+1, P is the search space, η(p i ) is the heuristic information, τ is the pheromone attenuation coefficient, To select the fitness function F ant (p i ) The maximum spectrum resource allocation scheme, p i is the spectrum resource allocation scheme of the i-th ant; S75. Through the iterative optimization of the ant colony algorithm, in each round of iteration, the ants adjust the path according to the historical pheromone and the current spectrum sensing results, update the global pheromone, and obtain the optimal spectrum resource allocation plan: Among them, τ ij (t+1) is the pheromone concentration on the path (i, j) at the t+1th iteration, τ ij (t) is the pheromone concentration on the path (i, j) in the tth iteration, ρ is the pheromone volatility coefficient, Q is the total amount of pheromone released by each ant, The path p is chosen by the kth ant in the tth iteration. i The fitness value of , k is the index variable, and M is the total number of ants; S76. Implement spectrum resource scheduling according to the obtained optimal spectrum resource allocation plan, adjust the frequency band in real time, and maximize bandwidth utilization.

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