GFDM effective capacity optimization method and system for jungle channel

Abstract

The invention relates to the technical field of wireless communication, and particularly discloses a jungle channel-oriented GFDM effective capacity optimization method and system, and the method comprises the steps: obtaining an original binary data stream of a transmitting end, carrying out the preprocessing and demodulation of a signal received by a receiving end, obtaining a demodulated binary data stream, and carrying out the demodulation of the demodulated binary data stream. Calculating an error rate based on the original binary data stream and the demodulated binary data stream; the effective capacity is calculated based on the bit error rate, the effective capacity serves as a utility function, GFDM waveform design parameters are optimized step by step by applying a particle swarm algorithm, and the GFDM waveform design parameters comprise a roll-off coefficient and the number of subcarriers; the optimization of the GFDM effective capacity under the jungle channel can be realized, and the optimal effective capacity and the corresponding subcarrier number and pulse shaping filter coefficient can be obtained.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, specifically to a method and system for optimizing the effective capacity of GFDM for jungle channels. Background Technology

[0002] Forest fires have a severe impact on the natural environment and economic development, so it is imperative to make every effort to prevent them and minimize their adverse effects. Meanwhile, in recent years, there have been cases of people going missing while exploring forest and mountainous areas, requiring rescue. These situations and disasters necessitate the use of communication equipment to provide information services and location tracking for fire brigades and search and rescue personnel during forest and mountain rescue operations. Furthermore, considering construction costs and other factors, wireless communication is the primary method of communication in forests. However, due to factors such as foliage blocking the light, radio wave propagation loss in forests is very high, making reliable and timely transmission difficult to guarantee. Therefore, research on reliable and timely wireless transmission in forests or jungles is essential.

[0003] Overcoming the negative impacts of the jungle propagation environment and ensuring timely and reliable transmission are major challenges in jungle communication. Effective capacity, the maximum data transmission rate that a channel can stably support under specified quality of service constraints, is a core indicator for measuring reliable and timely transmission. Research on the effective capacity of GFDM, a novel multi-carrier communication technology, is scarce, and no studies on GFDM effective capacity specifically for jungle communication have been reported. Optimizing GFDM parameters to reduce bit error rate and increase effective capacity for the channel characteristics of jungle communication is a problem worthy of in-depth research. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for optimizing the effective capacity of GFDM for jungle channels, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for optimizing the effective capacity of GFDM for jungle channels, the method comprising: The original binary data stream from the transmitter is obtained, and the signal received from the receiver is preprocessed and demodulated to obtain the demodulated binary data stream. The bit error rate is calculated based on the original binary data stream and the demodulated binary data stream. The effective capacity is calculated based on the bit error rate, and the effective capacity is used as the utility function. The particle swarm optimization algorithm is applied to optimize the GFDM waveform design parameters step by step. The GFDM waveform design parameters include the roll-off factor and the number of subcarriers. The parameter optimization steps specifically include: Using the effective capacity as the fitness function of the particle swarm optimization algorithm and setting the roll-off coefficient as the decision variable, the optimal roll-off coefficient is obtained by iterative optimization through the particle swarm optimization algorithm within the preset range of roll-off coefficient values. The optimal roll-off coefficient is used as a fixed parameter, the effective capacity is used as the fitness function, and the number of subcarriers is set as the decision variable. Within the preset range of subcarrier numbers, the particle swarm optimization algorithm is used for iterative optimization to obtain the optimal number of subcarriers.

[0006] As a further embodiment of the present invention, the steps of obtaining the original binary data stream from the transmitting end, preprocessing and demodulating the signal received by the receiving end to obtain the demodulated binary data stream specifically include: The transmitter performs M-QAM modulation on the input binary data stream to obtain a complex symbol matrix; the complex symbol matrix is ​​then pulse-shaped through a raised cosine roll-off filter to generate a time-domain signal. A cyclic prefix is ​​added to the time-domain signal to obtain the transmitted signal; The transmitted signal is transmitted through the jungle channel and then received at the receiving end. The receiving end performs matched filtering and equalization on the received signal to recover the complex symbol estimation matrix, and then demodulates it to obtain the demodulated binary data stream.

[0007] As a further aspect of the present invention, the formula for calculating the bit error rate is: ; in, BER For bit error rate, b tx For the number of bits sent, b rx For the number of bits received, N This represents the total number of bits. µ The number of bits for each symbol.

[0008] As a further embodiment of the present invention, the effective capacity C(θ) The calculation formula is: ; ; In the formula, i For QoS index, BER For the system bit error rate, M This is the QAM modulation order. fs Sampling rate, a This is the roll-off factor.

[0009] As a further embodiment of the present invention, the iterative optimization step using the particle swarm optimization algorithm includes: Initialize the particle swarm, set the particle size, maximum number of iterations, inertia weight, and learning factor, and randomly generate the initial position and velocity of each particle in the solution space of the corresponding decision variables; Calculate the fitness value corresponding to the current position of each particle; Update the individual historical best position of each particle and the global historical best position of the entire particle swarm based on the fitness value. Update the velocity and position of each particle according to the velocity update formula and the position update formula; Determine whether the iteration termination condition is met. If it is met, terminate the iteration and output the global historical best position as the optimal solution. If it is not met, return to fitness calculation and enter the next round of iteration.

[0010] As a further aspect of the present invention, a jungle channel model is constructed before applying the particle swarm optimization algorithm; the jungle channel model simulates multipath fading using Rayleigh distribution and calculates path loss using a jungle path loss empirical model. PL The path loss PL The calculation formulas include: ; in, PL_dθ To be at the reference distance dth The base path loss measured at the location, d This represents the actual transmission distance. n To match the signal frequency f trunk diameter TD vegetation density Dc Related path loss index.

[0011] As a further embodiment of the present invention, the formula for calculating the path loss index n is as follows: ; in, The diameter of the tree trunk, Dc For vegetation density, k 1 is the fundamental correction factor determined by the frequency. k 2 is the basic path loss index.

[0012] The present invention also provides a GFDM effective capacity optimization system for jungle channels, the system comprising: The performance testing module is used to acquire the original binary data stream from the transmitter, preprocess and demodulate the signal received by the receiver to obtain the demodulated binary data stream, and calculate the bit error rate based on the original binary data stream and the demodulated binary data stream. The parameter optimization module is used to calculate the effective capacity based on the bit error rate, and use the effective capacity as the utility function to apply the particle swarm optimization algorithm to optimize the GFDM waveform design parameters step by step. The GFDM waveform design parameters include the roll-off factor and the number of subcarriers. The parameter optimization steps specifically include: Using the effective capacity as the fitness function of the particle swarm optimization algorithm and setting the roll-off coefficient as the decision variable, the optimal roll-off coefficient is obtained by iterative optimization through the particle swarm optimization algorithm within the preset range of roll-off coefficient values. The optimal roll-off coefficient is used as a fixed parameter, the effective capacity is used as the fitness function, and the number of subcarriers is set as the decision variable. Within the preset range of subcarrier numbers, the particle swarm optimization algorithm is used for iterative optimization to obtain the optimal number of subcarriers.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: Based on the various influences of jungle environment on signal transmission, the present invention uses GFDM technology to construct an integrated communication solution that integrates adaptive utility function and dynamic parameter optimization. The solution uses particle swarm optimization algorithm to optimize the key parameters of GFDM system and finally obtain the optimal parameter configuration of the system, thereby effectively improving the overall communication quality of radio waves in jungle scenarios.

[0014] This invention allows for targeted parameter design based on different performance requirements. Relying on the high-precision ranging and positioning technology of GFDM signals, it can provide accurate location information for personnel and equipment. At the same time, it can achieve efficient data transmission and stable environmental perception, thereby comprehensively improving the communication and perception performance of the GFDM system in complex jungle environments and achieving the goal of optimal parameter configuration of the GFDM communication system in multiple scenarios. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0016] Figure 1 Different embodiments of the present invention a A graph showing the numerical trend of the utility function under a given value.

[0017] Figure 2 The graph shows the numerical trend of the utility function under different K values, as provided in the embodiments of the present invention. Detailed Implementation

[0018] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0019] In this embodiment of the invention, a method for optimizing the effective capacity of GFDM for jungle channels is provided, the method comprising: The original binary data stream from the transmitter is obtained, and the signal received from the receiver is preprocessed and demodulated to obtain the demodulated binary data stream. The bit error rate is calculated based on the original binary data stream and the demodulated binary data stream. The effective capacity is calculated based on the bit error rate, and the effective capacity is used as the utility function. The particle swarm optimization algorithm is applied to optimize the GFDM waveform design parameters step by step. The GFDM waveform design parameters include the roll-off factor and the number of subcarriers. The parameter optimization steps specifically include: Using the effective capacity as the fitness function of the particle swarm optimization algorithm and setting the roll-off coefficient as the decision variable, the optimal roll-off coefficient is obtained by iterative optimization through the particle swarm optimization algorithm within the preset range of roll-off coefficient values. The optimal roll-off coefficient is used as a fixed parameter, the effective capacity is used as the fitness function, and the number of subcarriers is set as the decision variable. Within the preset range of subcarrier numbers, the particle swarm optimization algorithm is used for iterative optimization to obtain the optimal number of subcarriers.

[0020] In this embodiment, a performance testing module is built in the GFDM jungle communication system. The receiver will test the bit error rate of the GFDM jungle communication system based on the known pilot signal sent by the transmitter, which will be used to build other evaluation indicators in the future.

[0021] The number of subcarriers in a GFDM system is a core parameter for balancing transmission performance (throughput, anti-fading, positioning accuracy), system latency, implementation complexity, and adaptability to specific scenarios such as jungles. Its value directly determines the granularity of frequency domain resource allocation and the overall communication sensing efficiency.

[0022] In GFDM systems for jungle communication, the number of subcarriers and the bit error rate (BER) are dynamically correlated. Jungle environments exhibit severe multipath propagation and frequency-selective fading. Increasing the number of subcarriers appropriately allows the system to distribute the signal across more subcarriers, reducing the probability of a single subcarrier being affected by fading through spectral diversity, thus improving the BER. However, GFDM itself sacrifices strict subcarrier orthogonality. If the number of subcarriers is too high, the frequency spacing between subcarriers decreases, and the time-frequency characteristics of the jungle channel further amplify inter-carrier interference. Simultaneously, the system's sensitivity to frequency offset and phase noise increases, ultimately leading to a significant increase in the BER. Conversely, if the number of subcarriers is too low, the bandwidth of a single subcarrier increases, making it more susceptible to jungle frequency-selective fading, and insufficient utilization of spectrum resources also results in a high BER.

[0023] In a preferred embodiment of the present invention, the steps of obtaining the original binary data stream from the transmitting end, preprocessing and demodulating the signal received by the receiving end to obtain the demodulated binary data stream specifically include: The transmitter performs M-QAM modulation on the input binary data stream to obtain a complex symbol matrix; the complex symbol matrix is ​​then pulse-shaped through a raised cosine roll-off filter to generate a time-domain signal. A cyclic prefix is ​​added to the time-domain signal to obtain the transmitted signal; The transmitted signal is transmitted through the jungle channel and then received at the receiving end. The receiving end performs matched filtering and equalization on the received signal to recover the complex symbol estimation matrix, and then demodulates it to obtain the demodulated binary data stream.

[0024] In this embodiment, on the transmitting side, the input binary data stream is first mapped via M-QAM modulation to obtain complex symbols. (C represents the complex number set, K represents the number of subcarriers, and M represents the number of subsymbols); then, the complex symbol is passed through a raised cosine roll-off filter to perform pulse shaping, ultimately generating a time-domain signal. x ( t ).

[0025] The frequency response function of the raised cosine roll-off filter is: ; In the formula T s For symbol period, R s For symbol transmission rate, and , f Let be the frequency, α be the roll-off factor, and ; In a jungle communication environment, the received signal is: ; In the formula, This indicates that the receiving end has received... x ( t Time-domain data of the signal, A unit impulse response model representing a jungle communication channel. This represents noise, and it has a mean of 0 and a variance of . Gaussian noise; At the sending end, let the original symbol sequence to be transmitted be... ,in It is the sequence length, and at the same time... To represent the length of the symbol corresponding to the cyclic prefix, the symbol after adding the cyclic prefix is... Represented as: ; Assume the impulse response of the channel is , length is L h Then, after adding the cyclic prefix, the signal received by the receiver will be... It can be represented as: ; In the above formula, Additive noise present in the channel; pre-added cyclic prefix c.p. Its core function is to avoid factors x ( n - l )and x ( n Inter-symbol interference (ISI) caused by the overlap of symbols between them is prevented, thereby ensuring the integrity and demodulation of the received signal.

[0026] At the receiving end, preliminary signal processing is first performed through matched filtering and frequency domain equalization (if necessary) to recover the received signal D into a complex symbol. ; then on Perform M-QAM demodulation to restore the corresponding binary data stream.

[0027] In a preferred embodiment of the present invention, the formula for calculating the bit error rate is as follows: ; in, BER For bit error rate, b tx For the number of bits sent, b rx For the number of bits received, N This represents the total number of bits. µ The number of bits per symbol (determined by the M-QAM modulation order). i Indicates from 1 toN The number.

[0028] In this embodiment, the system's bit error rate BER Then by comparing the original binary data stream from the transmitter... b tx The binary data stream obtained by demodulation at the receiving end b rx The bit difference is calculated, and the core calculation logic is the ratio of the number of erroneous bits to the total number of bits.

[0029] For bit error rate (BER), the roll-off factor a It works by influencing inter-symbol interference; a When the value is small, the raised cosine roll-off filter has a long tail in its impulse response, resulting in large inter-symbol interference. The receiver is susceptible to interference from adjacent symbols, which increases the bit error rate. a When the bandwidth is large, the tail is short and inter-symbol interference is reduced. However, the increased bandwidth may lead to a decrease in signal-to-noise ratio, which in turn increases the bit error rate. Therefore, it is necessary to select an appropriate bandwidth based on the actual channel conditions. a The value is used to achieve a balance between inter-symbol interference and signal-to-noise ratio in order to reduce the bit error rate; Symbol transmission rate R s Based on the number of subcarriers K The symbol period and the proportion of the cyclic prefix β are jointly determined, and the calculation formula is as follows: ; In the formula, Cyclic prefix time T CP With symbol period T S The ratio of .

[0030] As a preferred embodiment of the present invention, the effective capacity C(θ) The calculation formula is: ; ; In the formula, i For QoS index, BER For the system bit error rate, M This is the QAM modulation order. fs Sampling rate, a This is the roll-off factor.

[0031] In this embodiment, effective capacity is the maximum constant arrival rate that the service rate can support under the constraint of a specified QoS index θ. Effective capacity quantifies the upper limit of the service load that the system can stably support, and is a precise description of the system's carrying capacity under QoS constraints. It serves as a bridge to balance service randomness and service reliability.

[0032] Effective capacity exhibits a monotonic relationship with bit error rate (BER). As BER decreases, effective capacity continuously increases. Effective capacity is an indicator that describes the maximum constant data arrival rate that a random service system can stably support under statistical QoS (Quality of Service) constraints. Essentially, effective capacity is an extension of traditional channel capacity: traditional channel capacity only describes the theoretical maximum rate without QoS constraints, while effective capacity combines the randomness of the system's service rate (such as time-varying fading of the wireless channel) with the QoS requirements in practical applications (such as latency limits and packet loss rate thresholds), reflecting the actual available capacity of the system while meeting specified QoS requirements.

[0033] Effective capacity decreases monotonically with increasing bit error rate. In communication systems, an increase in bit error rate directly reduces effective capacity. To improve effective capacity, it is necessary to reduce bit error rate (e.g., by optimizing modulation, coding, and channel conditions).

[0034] Effective capacity quantifies the trade-off between QoS constraints and system capacity: the service rate of real-world systems (such as wireless communication and network transmission) fluctuates randomly (for example, in jungle communication, the transmission rate of a GFDM system changes due to multipath fading), while application scenarios often have strict QoS requirements; effective capacity introduces a QoS index. ( The larger the value of θ, the stricter the QoS constraints. This quantifies the inverse relationship between the strength of QoS constraints and the maximum rate that the system can support: when QoS requirements are more stringent (θ increases), the effective capacity decreases; conversely, relaxing QoS constraints can increase the effective capacity.

[0035] In fields such as communications and networking, effective capacity is the core basis for resource allocation and parameter design: In a jungle GFDM system, the effective capacity can be calculated using the effective capacity formula, combined with parameters such as bit error rate, number of subcarriers, and roll-off factor, to select the optimal parameter combination that can simultaneously meet QoS requirements and transmission rate demands. In network scheduling, effective capacity helps determine bandwidth allocation strategies for different service flows (such as real-time voice and non-real-time data), ensuring that the QoS constraints of high-priority services are met.

[0036] Effective capacity can assess the actual performance of a random service system: traditional capacity metrics (such as Shannon capacity) are theoretical upper limits under ideal and unconstrained conditions, and cannot reflect the system’s performance in a real random environment.

[0037] Effective capacity is based on the statistical characteristics of service rate (such as stationarity and correlation), which is closer to the actual scenario: for example, the service rate of a wireless channel is time-varying, and effective capacity can accurately describe the maximum transmission rate of the channel while ensuring that the latency of 99.9% of data packets does not exceed 10ms, providing a more practical standard for system performance evaluation.

[0038] In a preferred embodiment of the present invention, a jungle channel model is constructed before optimization using the particle swarm optimization algorithm. The jungle channel model simulates multipath fading using Rayleigh distribution and calculates the path loss PL using a jungle path loss empirical model. PL The calculation formulas include: ; in, PL_dθ To be at the reference distance dth The base path loss measured at the location, d This represents the actual transmission distance. n To match the signal frequency f trunk diameter TD vegetation density Dc Related path loss index.

[0039] The path loss index n The calculation formula is: ; in, The diameter of the tree trunk, Dc For vegetation density, k 1 is the fundamental correction factor determined by the frequency. k 2 is the basic path loss index.

[0040] In this embodiment, the jungle channel is a typical complex variable-parameter wireless channel. Its characteristics are jointly determined by factors such as jungle vegetation obstruction, terrain undulation, and electromagnetic environment. Its core features include severe path loss influenced by multiple factors, frequency-band dependent propagation modes, significant multipath fading, and limited communication distance. Constructing a GFDM communication system in the jungle channel can be achieved by using a superposition of Rayleigh distribution and an empirical model of jungle path loss for simulation. The Rayleigh power spectral density function is: ; in, For channel gain, This represents the channel variance.

[0041] The jungle path loss empirical model is derived from empirical fitting of experimental data. The formula for calculating path loss PL is: ; ; ; ; ; In the formula, f The signal frequency (GHz) The transmission distance (m) The diameter of the tree trunk (cm) Dc For vegetation density, FD For vegetation cover, refer to distance. Basic path loss index k 2 = 2.27.

[0042] In a preferred embodiment of the present invention, the iterative optimization step using the particle swarm optimization algorithm includes: Initialize the particle swarm, set the particle size, maximum number of iterations, inertia weight, and learning factor, and randomly generate the initial position and velocity of each particle in the solution space of the corresponding decision variables; Calculate the fitness value corresponding to the current position of each particle; Update the individual historical best position of each particle and the global historical best position of the entire particle swarm based on the fitness value. Update the velocity and position of each particle according to the velocity update formula and the position update formula; Determine whether the iteration termination condition is met. If it is met, terminate the iteration and output the global historical best position as the optimal solution. If it is not met, return to fitness calculation and enter the next round of iteration.

[0043] In this embodiment, for the GFDM jungle communication system, while keeping other waveform parameters constant, the particle swarm optimization algorithm is integrated into the communication system. The number of subcarriers and the roll-off coefficient are optimized using a utility function. The effective capacity is used as the fitness function of the particle swarm optimization algorithm, and the roll-off coefficient is set as the decision variable, with its value range limited to (…). a min , a max )( a min and a max (The minimum and maximum values ​​of the custom roll-off coefficients are defined). By continuously adjusting the values ​​of the decision variables, the fitness function converges to the optimal value.

[0044] The solution process for optimizing the roll-off factor of the pulse shaping filter is as follows: First, determine the core parameters of the algorithm, including the particle swarm size (i.e., the total number of particles, taken as 50), the maximum number of iterations (taken as 100), the inertia weight (taken as [0.3 0.8]), and the learning factor. c 1 and c 2 (All set to 1.5), Boundary range of velocity and position, parameter settings: K is the number of subcarriers, and the simulation value is set to 256; cp is the cyclic prefix, and the simulation value is set to 0.2. a max The simulation value is set to 0.95 as the maximum roll-off factor; a min The minimum roll-off factor is set to 0.05 in the simulation; the signal-to-noise ratio (SNR) is set to 25 dB in the simulation; the number of modulation bits (mu) is set to 4 in the simulation; the number of sub-symbols (M) is set to 15 in the simulation; and the trunk diameter (TD) is set to 0.1 in the simulation. Dc The simulated value for vegetation density is set to 2.6; FD The simulated value for vegetation coverage is 75; the simulated value for QoS index is 1. d For the transmission distance, the simulation value is set to 600 (m); f The signal frequency is set to 0.87 GHz in the simulation. fs The sampling rate is set to 5MHz in the simulation.

[0045] Then, within the solution space of the problem to be optimized, an initial position (corresponding to a candidate solution to the problem) and an initial velocity are randomly generated for each particle, and the initial position of each particle is set as its individual optimal position. p b Then, the optimal position is selected from the individual optimal positions of all particles and used as the global optimal position for the entire population. g b .

[0046] For each particle's current position, substitute it into the objective function of the problem to calculate its fitness value.

[0047] For each particle, compare its current fitness value with its historical value. p b The corresponding fitness value; if the current value is better, then the particle's fitness value is... p b Update to the current position; then iterate through all particles. p b If a certain p b Its fitness value is better than the current g b The fitness value will then determine the population's... g b Updated to thisp b The location.

[0048] Update the velocity and position of each particle: Speed ​​updates: ; Location update: ; Particle swarm optimization (PSO) guides particles toward their individual optimal positions by iteratively updating the velocity and position of each particle. p b and global optimal g b The particles converge, eventually reaching the optimal solution to the problem. Velocity determines the direction and step size of particle movement, while position corresponds to candidate solutions. Updating these two parameters is the core of PSO (Problem Solving), requiring strict adherence to classical formulas and the application of boundary constraints.

[0049] In Particle Swarm Optimization (PSO), the velocity update of each particle is a weighted combination of "inertia + individual learning + social learning," while the position update is a direct summation of velocities. Furthermore, boundary constraints must be used to ensure that the particles remain within the solution space. This update logic embodies the core idea of ​​swarm collaboration and balances the exploratory and convergent aspects of optimization through random numbers and weights, making it a crucial element in PSO optimization.

[0050] Check if the termination conditions are met: first, the preset maximum number of iterations has been reached; second, ... g b The corresponding fitness value meets the accuracy requirements of the problem (e.g., the change in fitness is less than the threshold for multiple consecutive generations). If this is met, the iteration stops; otherwise, it returns to the "fitness calculation" step and proceeds to the next iteration.

[0051] After the iteration terminates, the final g b The position is output as the optimal solution to the problem, and its corresponding fitness value can be output simultaneously to complete the entire optimization process and obtain the final value of the optimized utility function F.

[0052] The roll-off factor and time-frequency locality of a filter directly determine the system's anti-interference capability: a higher roll-off factor provides steeper out-of-band attenuation, effectively suppressing inter-carrier interference caused by dense multipath propagation in jungle environments, but it also extends the time-domain response of symbols, exacerbating inter-symbol interference in time-varying channels; a lower roll-off factor, while compressing time-domain spread and mitigating the impact of Doppler spread, relaxes spectral constraints, leading to increased adjacent-channel interference. The choice of filter duration is also crucial. Longer filters provide better spectral concentration, combating frequency-selective fading in jungle environments, but increase system delay and amplify the effects of channel time-varying characteristics; shorter filters offer faster response and are more robust to channel variations, but at the cost of sacrificing frequency resolution and interference suppression capabilities. In particular, under the uniquely fast time-varying conditions of jungle channels, filters need to possess good localization characteristics in both the time and frequency domains to balance the dual challenges of delay spread and Doppler spread. Therefore, optimizing filter coefficients must comprehensively consider the multipath intensity, time-varying rate, and interference characteristics of jungle channels. By adjusting the coefficients, an optimal balance must be found between spectral efficiency, interference suppression, and time-varying robustness to minimize the system bit error rate. Effective capacity and bit error rate have a monotonically decreasing relationship, leading to the maximum value of effective capacity.

[0053] Observe the different through MATLAB simulation. a The numerical trend of the utility function under the given value, such as Figure 1 As shown. Figure 1 It records the best individuals in each generation. a The value and its corresponding optimal utility function value are calculated. As the number of iterations increases, the curve gradually converges, and the utility function shows an overall decreasing trend, indicating successful optimization. After optimization, the final solution of the system is obtained as follows: a opt =0.0527, corresponding to the utility function, i.e., the effective capacity F = 4.893721. This result indicates that when the pulse filter roll-off factor in the GFDM parameter design is 5.27%, the optimal effective capacity can be obtained in the jungle communication system.

[0054] Optimization for the number of subcarriers K: The optimal solution obtained by optimizing the roll-off factor in the above steps to achieve the best utility function is selected. a opt ;Will a opt As a constant waveform parameter in the GFDM jungle communication system, the other constant waveform parameters remain unchanged, and the roll-off factor of the pulse shaping filter is set to 0.0527. The remaining parameters are set as follows: The optimal roll-off factor is set to 0.0527 in the simulation; cp is the cyclic prefix, and the simulation value is set to 0.2. Kmax The simulation value is set to 512 for the maximum number of subcarriers. K min The minimum number of subcarriers is set to 16 in the simulation; the signal-to-noise ratio (SNR) is set to 25 dB in the simulation; the number of modulation bits (mu) is set to 4 in the simulation; the number of sub-symbols (M) is set to 15 in the simulation; and the trunk diameter (TD) is set to 0.1 in the simulation. Dc The simulated value for vegetation density is set to 2.6; FD The simulated value for vegetation coverage is 75; the simulated value for QoS index is 1. d For the transmission distance, the simulation value is set to 600 (m); f The signal frequency is set to 0.87 GHz in the simulation. fs The sampling rate is set to 5MHz in the simulation.

[0055] For the same utility function, the particle swarm optimization algorithm optimizes the number of subcarriers K, while limiting the range of values ​​to [ K min , K max ](in K min The minimum number of custom subcarriers. K max (Maximum number of custom subcarriers).

[0056] First, determine the core parameters of the algorithm, including the particle swarm size (i.e., the total number of particles, taken as 50), the maximum number of iterations (taken as 100), the inertia weight (taken as [0.3 0.8]), and the learning factor. c 1 and c 2 (both set to 1.5), the boundary range of velocity and position; then, within the solution space of the problem to be optimized, randomly generate an initial position (corresponding to a candidate solution of the problem) and an initial velocity for each particle, and simultaneously set the initial position of each particle as its individual optimal position. Then, the optimal position is selected from the individual optimal positions of all particles and used as the global optimal position for the entire population. g b .

[0057] For each particle's current position, substitute it into the objective function of the problem to calculate its fitness value.

[0058] For each particle, compare its current fitness value with its historical value. p b The corresponding fitness value; if the current value is better, then the particle's fitness value is... p b Update to the current position; then iterate through all particles. pb If a certain p b Its fitness value is better than the current g b The fitness value will then determine the population's... g b Updated to this p b The location.

[0059] Update the velocity and position of each particle: Speed ​​updates: ; Location update: ; Check if the termination conditions are met: first, the preset maximum number of iterations has been reached; second, ... g b The corresponding fitness value meets the accuracy requirements of the problem (e.g., the change in fitness is less than the threshold for multiple consecutive generations). If this is met, the iteration stops; otherwise, it returns to the "fitness calculation" step and proceeds to the next iteration.

[0060] After the iteration terminates, the final g b The position is output as the optimal solution to the problem, and its corresponding fitness value can be output simultaneously to complete the entire optimization process and obtain the final value of the optimized utility function F.

[0061] In jungle channel environments, the relationship between the number of subcarriers and the bit error rate (BER) in GFDM systems exhibits unique complexity. Jungle channels are characterized by severe multipath fading, dense scatterers, and high time-varying characteristics, leading to a nonlinear impact of subcarrier number variations on the BER. When the number of subcarriers is low, a wider subcarrier spacing, while able to resist frequency-selective fading, still generates severe inter-symbol interference under dense multipath conditions, resulting in a persistently high BER. As the number of subcarriers increases, a narrower subcarrier spacing allows for more precise spectrum segmentation, better adapting to the complex frequency response variations in jungle channels and improving BER performance through frequency diversity. However, once the number of subcarriers exceeds a certain threshold, extremely narrow subcarrier spacing makes the system highly sensitive to Doppler shift and phase noise common in jungle environments. Simultaneously, dense subcarrier configurations exacerbate inter-carrier interference under strong multipath conditions, causing the BER to rise again. Furthermore, severe shadowing and vegetation obstruction in jungle channels introduce slow fading, requiring the system to consider adaptive power adjustment during subcarrier allocation, further impacting the relationship between the BER and the number of subcarriers. In jungle channels, there exists an optimal range of subcarrier numbers within which the system achieves the best balance between frequency diversity gain and interference suppression, resulting in the lowest bit error rate. Effective capacity and bit error rate have a monotonically decreasing relationship, leading to the maximum effective capacity.

[0062] The numerical trend of the utility function under different K values ​​was observed through MATLAB simulation, such as... Figure 2 As shown. Figure 2 The K value and its corresponding optimal utility function value for each generation of the best individual were recorded. As the number of iterations increased, the curve gradually converged, and the utility function showed an overall decreasing trend, indicating successful optimization. After optimization, the final solution of the system was obtained as follows: K opt =18, corresponding to the utility function, i.e., the effective capacity F=5.703782. This result shows that when the pulse filter roll-off factor is 5.27% and the number of subcarriers is 18 in the GFDM parameter design, the optimal effective capacity can be obtained in the jungle communication system.

[0063] This invention also provides a GFDM effective capacity optimization system for jungle channels, the system comprising: The performance testing module is used to acquire the original binary data stream from the transmitter, preprocess and demodulate the signal received by the receiver to obtain the demodulated binary data stream, and calculate the bit error rate based on the original binary data stream and the demodulated binary data stream. The parameter optimization module is used to calculate the effective capacity based on the bit error rate, and use the effective capacity as the utility function to apply the particle swarm optimization algorithm to optimize the GFDM waveform design parameters step by step. The GFDM waveform design parameters include the roll-off factor and the number of subcarriers. The parameter optimization steps specifically include: Using the effective capacity as the fitness function of the particle swarm optimization algorithm and setting the roll-off coefficient as the decision variable, the optimal roll-off coefficient is obtained by iterative optimization through the particle swarm optimization algorithm within the preset range of roll-off coefficient values. The optimal roll-off coefficient is used as a fixed parameter, the effective capacity is used as the fitness function, and the number of subcarriers is set as the decision variable. Within the preset range of subcarrier numbers, the particle swarm optimization algorithm is used for iterative optimization to obtain the optimal number of subcarriers.

[0064] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing effective capacity of GFDM for jungle channel, characterized in that, The method comprises: acquiring a binary data stream at a transmitting end, pre-processing and demodulating a signal received at a receiving end to obtain a demodulated binary data stream, and calculating a bit error rate based on the binary data stream at the transmitting end and the demodulated binary data stream; calculating an effective capacity based on the bit error rate, taking the effective capacity as a utility function, and stepwise optimizing GFDM waveform design parameters by using a particle swarm algorithm, wherein the GFDM waveform design parameters include a roll-off coefficient and a subcarrier number; the parameter optimization step specifically comprises: taking the effective capacity as a fitness function of the particle swarm algorithm, taking the roll-off coefficient as a decision variable, and iteratively optimizing the roll-off coefficient in a preset roll-off coefficient value range by using the particle swarm algorithm to obtain an optimal roll-off coefficient; taking the obtained optimal roll-off coefficient as a fixed parameter, taking the effective capacity as a fitness function, taking the subcarrier number as a decision variable, and iteratively optimizing the subcarrier number in a preset subcarrier number value range by using the particle swarm algorithm to obtain an optimal subcarrier number. 2.The method of claim 1, wherein The step of acquiring a binary data stream at a transmitting end, pre-processing and demodulating a signal received at a receiving end to obtain a demodulated binary data stream specifically comprises: modulating an input binary data stream at the transmitting end by M-QAM to obtain a complex symbol matrix; performing pulse shaping on the complex symbol matrix by a raised cosine roll-off filter to generate a time domain signal; adding a cyclic prefix to the time domain signal to obtain a transmission signal; after the transmission signal is transmitted through a jungle channel, a receiving end obtains a received signal; the receiving end performs matched filtering and equalization processing on the received signal to recover a complex symbol estimation matrix, and demodulates the complex symbol estimation matrix to obtain a demodulated binary data stream.

3. The method of Claim 1, wherein The bit error rate calculation formula is: ； wherein, BER BER is the bit error rate, b tx N is the number of transmitted bits, b rx N is the number of received bits, N N is the total number of bits, µ N is the number of bits per symbol.

4. The method of Claim 1, wherein the effective capacity C(θ) The calculation formula is: ； ； In the formula, θ is a QoS index, BER is a system error rate, M is a QAM modulation order, fs is a sampling rate, a is a roll-off factor.

5. The method of Claim 1, wherein The step of iteratively optimizing by using the particle swarm algorithm comprises: initializing a particle swarm, setting a particle size, a maximum iteration number, an inertia weight, a learning factor, and randomly generating an initial position and a speed of each particle in a solution space corresponding to a decision variable; calculating a fitness value corresponding to a current position of each particle; updating an individual historical optimal position of each particle and a global historical optimal position of the entire particle swarm according to the fitness value; updating a speed and a position of each particle according to a speed update formula and a position update formula; judging whether an iteration termination condition is met, and if the iteration termination condition is met, terminating the iteration and outputting the global historical optimal position as an optimal solution, and if the iteration termination condition is not met, returning to fitness calculation and entering a next round of iteration.

6. The method of Claim 1, wherein Before the particle swarm algorithm is applied for optimization, a jungle channel model is constructed; the jungle channel model simulates multipath fading by using a Rayleigh distribution, and calculates a path loss PL by using a jungle path loss empirical model, and a calculation formula of the path loss PL is comprises: ； in, PL_dθ To be at the reference distance dθ The base path loss measured at the location, d denoted as the actual transmission distance, and n is the path loss index related to the signal frequency f, trunk diameter TD, and vegetation density Dc.

7. The method of Claim 1, wherein A calculation formula of the path loss index n is: ； wherein, D is the trunk diameter, Dc D is the vegetation density, k 1 is a base correction factor determined by frequency, k 2 is a base path loss exponent.

8. A system for jungle channel oriented GFDM effective capacity optimization, for implementing the method of any one of claims 1-7, characterized in that, The system comprises: a performance test module configured to acquire a binary data stream at a transmitting end, pre-process and demodulate a signal received at a receiving end to obtain a demodulated binary data stream, and calculate a bit error rate based on the binary data stream at the transmitting end and the demodulated binary data stream. The parameter optimization module is configured to calculate an effective capacity based on the bit error rate, take the effective capacity as a utility function, and step by step optimize GFDM waveform design parameters including a roll-off coefficient and a subcarrier number by using a particle swarm algorithm. The parameter optimization step specifically includes: Taking the effective capacity as a fitness function of the particle swarm algorithm, setting the roll-off coefficient as a decision variable, and performing iterative optimization in a preset roll-off coefficient value interval by using the particle swarm algorithm to obtain an optimal roll-off coefficient. Taking the obtained optimal roll-off coefficient as a fixed parameter, taking the effective capacity as a fitness function, setting the subcarrier number as a decision variable, and performing iterative optimization in a preset subcarrier number value interval by using the particle swarm algorithm to obtain an optimal subcarrier number.

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