Coherent free space optical communication system based on improved grey wolf algorithm

Abstract

The invention relates to the technical field of optical communication, and provides a coherent free space optical communication system based on an improved grey wolf algorithm in order to solve the technical problems that an existing grey wolf algorithm is low in convergence speed and prone to falling into local optimum. An improved DLH strategy based on a dynamic elite enhancement mechanism is combined, the global exploration and local development capability of the algorithm is effectively balanced, and the problem that a traditional algorithm is prone to falling into local optimum when processing high-order Zernike aberration is solved; meanwhile, a population scale reduction NPSR strategy based on a nonlinear S-shaped function is designed, the population scale is rapidly reduced in a nonlinear mode in the later stage of iteration, and the running time of the SLAO system is remarkably shortened.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optical communication technology, in particular to a coherent free space optical communication system based on an improved grey wolf algorithm. BACKGROUND

[0002] The rapid increase in demand for ultra-high-speed and large-capacity connections in communication technology has made traditional wireless communication face the challenges of spectrum congestion and electromagnetic interference. Coherent free space optical communication (CFSOC) is attracting attention because of its advantages over traditional free space optical communication in terms of transmission distance, sensitivity and communication capacity. Compared with the non-coherent FSOC system using intensity detection / direct detection and binary on-off keying modulation, the CFSOC system has many advantages such as flexible modulation / demodulation, high receiver sensitivity, and longer communication distance without relay.

[0003] The technology uses laser as carrier and atmosphere as transmission medium. However, atmospheric turbulence can distort the amplitude and wavefront of the laser carrier signal, resulting in a decrease in mixed efficiency (ME) and an increase in bit error rate (BER). Adaptive optics (AO) technology compensates for the wavefront phase aberration caused by atmospheric turbulence to improve the performance of CFSOC. The sensorless adaptive optics (SLAO) method of wavefront adaptive optics has received widespread attention in the wavefront distortion correction of CFSOC systems because it does not require a wavefront sensor.

[0004] Existing sensorless adaptive optics SLAO technology, such as model-based methods, has high implementation costs. Deep learning methods have weak generalization ability and poor correction effect in strong turbulence. While the widely used model-free iterative algorithm (such as the random parallel gradient descent algorithm and its improved version) faces the problems of slow convergence speed, the need for a large number of iterations, and the risk of falling into local optimum, making it difficult to achieve efficient and robust wavefront distortion correction in strong atmospheric turbulence environments.

[0005] Grey wolf algorithm (GWO) is a swarm intelligence optimization algorithm that simulates the hunting behavior of grey wolves and is used to solve various optimization problems. The traditional grey wolf algorithm is limited by the lack of population diversity, resulting in slow convergence speed and the risk of falling into local optimum. SUMMARY

[0006] The present application aims to solve the technical problems of slow convergence speed and the risk of falling into local optimum of existing algorithms and proposes a coherent free space optical communication system based on an improved grey wolf algorithm.

[0007] The coherent free space optical communication system based on the improved grey wolf algorithm includes a wavefront sensorless adaptive optical system for correcting wavefront distortion, and the wavefront sensorless adaptive optical system comprises: A high-speed camera is used to collect a wavefront PSF image and transmit the preprocessed PSF image to a wavefront controller. The wavefront controller is used to identify the wavefront characteristics carried by the PSF image, generate a voltage signal for adjusting the wavefront corrector to correct the wavefront distortion according to the wavefront characteristics. The wavefront corrector receives the voltage signal and corrects the wavefront distortion, and outputs a corrected optical signal. The wavefront controller runs the improved grey wolf algorithm internally, and the improved grey wolf algorithm combines speed-guided update, dimension learning hunting strategy and nonlinear population size reduction strategy on the basis of the structure of the grey wolf algorithm, so as to improve the efficiency and accuracy in the application of the wavefront sensorless adaptive optical system wavefront distortion correction.

[0008] Technical effects: The improved dimension learning hunting strategy is combined, and the nonlinear population size reduction technology is further integrated, so as to construct the improved grey wolf algorithm (VDNGWO), which comprehensively integrates the three core strategies of speed-guided update, dimension learning hunting (DLH) and nonlinear population size reduction (NPSR), and further optimizes the algorithm performance through multi-strategy competition selection and elite enhancement mechanism. The speed-guided position update equation is constructed by introducing the inertia weight and the nonlinear decay model, and the improved DLH strategy based on the dynamic elite enhancement mechanism is combined, so as to effectively balance the global exploration and local development ability of the algorithm, and solve the problem that the traditional algorithm is easy to fall into local optimum when processing high-order Zernike aberration. Meanwhile, the population size reduction (NPSR) strategy based on the nonlinear S-shaped function is designed. Different from the traditional linear reduction, the strategy maintains sufficient population diversity in the early stage of the algorithm to ensure the search breadth, and quickly reduces the population size in the later stage of iteration through the nonlinear way, which significantly reduces the calculation overhead and running time of the SLAO system.

[0009] Compared with the existing mainstream wavefront-free adaptive optical control algorithm (such as SPGD algorithm and conventional GWO algorithm), the present application significantly improves the convergence speed and calculation efficiency. The prior art usually faces the problem of many iterations and large calculation redundancy, which is difficult to meet the real-time requirements of high-speed communication. By introducing NPSR, the present application can maintain population diversity in the early stage of algorithm running to ensure the search breadth, and nonlinearly reduce the population size in the later stage to reduce the calculation overhead. Simulation results show that, under the premise of reaching the same correction accuracy (such as RMS≤0.1), the running time of the present application is significantly better than that of the traditional algorithm, effectively solving the technical bottleneck of slow response speed of the SLAO system.

[0010] In addition, the present application greatly enhances the robustness and correction accuracy of the system in strong atmospheric turbulence environment. In view of the defect that the existing algorithm is easily trapped in local optimal solution under strong turbulence interference, the present application constructs a speed-guided position updating mechanism and an improved dimension learning hunting (DLH) strategy, strengthens the ability of the algorithm to jump out of the local extreme value, and realizes the dynamic balance of global exploration and local development. Experiments prove that, even under strong turbulence conditions, the present application can still improve the ME of the CFSOC system to more than 0.99, and greatly reduce the BER, overcoming the shortcomings of the prior art that the correction effect is unstable under bad atmospheric channel.

[0011] These improvements systematically solve the problems of slow convergence speed and easy trapping into local optimal solution of the traditional GWO algorithm in dealing with complex optimization problems from three aspects of position updating mechanism, search strategy and population management, realize the dynamic balance of global exploration and local development, and significantly improve the efficiency and accuracy of the algorithm in the application of wavefront distortion correction of adaptive optical system. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 It is a structural schematic diagram of the CFSOC system of the prior art with the SLAO system.

[0013] Figure 2 It is a hierarchical structure diagram of the existing grey wolf algorithm.

[0014] Figure 3 It is a running flowchart of the VDNGWO algorithm in the wavefront controller of the present embodiment.

[0015] Figure 4 It is an initial Zernike coefficient diagram of different wavefront aberrations under strong turbulence and weak turbulence, respectively.

[0016] Figure 5 It is a diagram of RMS change with iteration number under strong turbulence and weak turbulence, respectively.

[0017] Figure 6 It is a comparison diagram of the effectiveness change of different improved components in the VDNGWO algorithm.

[0018] Figure 7 RMS comparison chart of VDNGWO, GWO, SPGD and hybrid algorithm under strong turbulence and weak turbulence at different iteration times.

[0019] Figure 8 Wavefront aberration phase and point spread function comparison chart under different turbulence intensities.

[0020] Figure 9 Wavefront aberration phase and point spread function chart under strong turbulence conditions with the change of iteration times.

[0021] Figure 10 Wavefront aberration phase and point spread function chart under weak turbulence conditions with the change of iteration times. DETAILED DESCRIPTION

[0022] The technical solutions of the present application will be described in detail below in combination with the drawings and preferred embodiments.

[0023] In this embodiment, a continuous surface deformable mirror (CSDM) is used as a wavefront corrector. The device is composed of a two-dimensional array of voltage-driven actuators distributed on the optical pupil surface and coupled with a continuous reflective surface. By adjusting the driving voltage of each actuator in real time, the mirror shape can respond to the change of wavefront aberration, thereby changing the light path distribution on the pupil surface and realizing the conjugate compensation of atmospheric turbulence aberration. The mirror deformation of CSDM can be represented as the linear superposition of the influence function of each actuator, where each influence function is theoretically approximated by a Gaussian mode, and the specific operation formula is as follows: (1) wherein represents the cross-linking value between adjacent actuators in CSDM, represents the normalized distance between adjacent actuators, represents the coordinate position of the th actuator in CSDM, represents the Gaussian function coefficient. Under the action of the driving voltage, the CSDM generates the following phase compensation for each actuator action: (2) wherein represents the control voltage of the th actuator, represents the number of units of CSDM actuators.

[0024] From the population characteristics, gray wolves have a significant colony living habits. Each standard wolf colony is usually composed of 5 to 12 individuals, and this medium-sized group structure achieves a delicate balance between resource utilization efficiency and group management complexity. It is particularly worth noting that its social hierarchy system, the gray wolf group follows a strict social hierarchy system in life. According to different levels, the wolf group can be divided into four levels, namely: alpha wolf (leader), beta wolf (sub-leader), delta wolf (ordinary member) and omega wolf (the lowest position wolf), and the gray wolf group hierarchy is as shown in Figure 2 The social structure and hunting strategy provide the basis for the mathematical model of the GWO algorithm. According to the social composition and behavior model of the gray wolf group in nature, the hierarchy system and hunting process of the gray wolf group are converted into position update formulas through mathematical modeling.

[0025] In the surrounding prey stage, formulas (3) and (4) mathematically model the process of the wolf group surrounding the prey: (3) (4) Wherein represents the current iteration number, represents the prey position, represents the gray wolf position. and represent the coefficients, which can be expressed as: (5) (6) Wherein is the convergence factor, and are random numbers in the interval [0, 1]. The GWO algorithm regards the positions of alpha, beta and delta wolves as optimal solutions during iteration. The three groups of optimal solutions will be saved, and the omega wolf is forced to update its own coordinates according to the position of the best search individual. The specific formula of this process is as follows: (7) (8) (9) In this embodiment, the VDNGWO algorithm is adopted, and its algorithm flow chart is as shown in Figure 3 , which specifically includes three core strategies: a. Velocity-based position update equation: In the GWO algorithm, the convergence factor is the key to balancing exploration and development. When When the value is large, the population tends to explore the global solution space more comprehensively; while when the value is small, it tends to develop locally near the current region. In the traditional GWO algorithm, the convergence factor adopts a linear decreasing strategy, gradually decreasing from the initial value 2 to 0, which is expressed as follows: (10) where denotes the maximum number of iterations, t denotes the current number of iterations.

[0026] The simple linear decreasing strategy cannot effectively adapt to the complex changes of search dynamics in the actual optimization search process. In this embodiment, the inertia weight is introduced, and the model is updated by a nonlinear decay coupled with the inertia weight, which is shown in the following equation: (11) (12) where is a random number in the interval [-1, 1]; is a random number in the interval [0, 1]; is the initial value of the weight, which is taken as 0.9; is the final value of the weight, which is taken as 0.1; e is the natural logarithm base, in order to construct a nonlinear curve and achieve normalization effect.

[0027] By simultaneously incorporating the global optimal solution and two randomly selected population solutions and in the speed update stage, the speed and position update formula is reconstructed to improve the local exploration efficiency. After optimization, the speed update of the individual gray wolf is driven by the synergistic effect of inertia inheritance, population memory guidance, and individual memory interaction, which is expressed as follows: (13) (14) In equation (13), is the cognitive factor (set to 1.5), is the social factor (set to 2.5), and are random numbers in the interval [0, 1]. The speed inertia term inherits the gray wolf speed information at the previous time step, where denotes the speed of . represents the global optimal solution of the current population. The population memory term​ Guiding the gray wolf towards its historical optimal position, individual memory items This introduces inter-individual interactions within the population by utilizing the random difference between an individual's current position and its historical position.

[0028] b. Improved Dimensional Learning-Based Hunting Strategy (DLH): DLH enhances the algorithm's exploration capabilities and maintains population diversity by improving the interaction between individual wolves and their neighbors and by introducing other randomly selected individuals from the pack to simulate individual hunting behavior.

[0029] In the DLH search strategy, new location The new location of an individual is calculated using formula (17), where the individual's new location is guided by its different neighbors and a wolf randomly selected from the population. During this process, the DLH search strategy also generates other reference locations to determine the wolf's new location, i.e. Therefore, the radius needs to be calculated. The radius passes through and The Euclidean distance between them is calculated as shown in formula (15): (15) Neighborhood set The result was then calculated using formula (16), which took into account... ,and Indicates that it is located at and The Euclidean distance between the wolves.

[0030] (16) Subsequently, multi-neighborhood learning is performed using formula (17). Specifically, A random number within the interval [0,1]. The Weiyou Neighborhood The Maintenance and Population The first wolf randomly selected from the middle The decision was made jointly by the two sides.

[0031] (17) Finally, when selecting the optimal solution, the improved candidate solutions of the strategy need to be considered and evaluated by comparing their fitness values, as shown in Equation (18).

[0032] (18) This embodiment employs a phased dynamic elite reinforcement mechanism based on the DLH strategy to further enhance the algorithm's adaptive optimization search capability and performance; this mechanism is referred to as the improved DLH. In the early search phase (t ≤ 25), individuals with poor fitness and performance in the population are reinitialized every three iterations. In the later iteration phase (t ≥ 40), candidate positions are finely adjusted by adding noise within 0.5% of the variance of the α wolf individuals, and fitness detection is combined to screen for better solutions.

[0033] c. Nonlinear population size reduction strategy (NPSR): By using a nonlinear sigmoid function instead of a linear function as the benchmark, this method can be applied globally within the algorithm, and its mathematical expression is as follows: (19) in Indicates the population size for the next iteration; This represents the initial population size, set to 30. This represents the minimum population size required for convergence, which is set to 3. This represents the inflection point, i.e., the number of iterations at which the rate of population size decline reaches its maximum value, and is set to 20. The control parameter representing the rate of decline determines the steepness of the population size change, and its value is 0.4.

[0034] In the CFSOC system, ME and BER are key metrics for evaluating communication performance. By analyzing these key metrics, the optimal control effect of the VDNGWO algorithm in the SLAO system can be evaluated. Assuming the laser source is a plane wave with uniform beam intensity, according to coherent detection theory, the total optical power of the photodetector can be calculated using the following formula: (20) in and These represent the amplitudes of OS and LO, respectively. and These represent the frequencies of OS and LO, respectively. This represents the area of ​​the receiving aperture. It is the phase difference between OS and LO, where and These represent the phases of OS and LO, respectively. In the CFSOC system, phase distortion caused by atmospheric turbulence... It can be considered as a fixed state, and its calculation expression is as follows: (twenty one) in This represents time-independent phase aberrations caused by atmospheric turbulence. This represents the modulation phase, which is independent of spatial coordinates. When = This is called zero-difference detection; when ≠ This is called heterodyne detection. Under zero-difference detection conditions, the measurement error of the CFSOC system is approximately equal to the Strel ratio (SR) of the far-field image, expressed by formula (22): (twenty two) SR is one of the parameters for measuring the quality of an optical system, defined as the ratio of the peak intensity of wavefront aberrations to the maximum intensity of ideal diffraction within the optical system. During propagation, the laser beam undergoes tilting and phase distortion due to atmospheric turbulence, thus increasing the BER. The expression for BER is given by the following equation: (twenty three) in Represents the complementary error function. This represents the signal-to-noise ratio under zero-difference detection conditions. Under atmospheric turbulence conditions... use The expression is as follows: (twenty four) in This represents the number of photons received per bit. delta This indicates the quantum efficiency of the detector. This represents the optical power used to synchronize the bit error rate at the receiver in a binary phase-shift keying (BPSK) receiver system. Therefore, under atmospheric turbulence conditions, a BPSK receiver system can be expressed as: (25) in eta This represents the average error in the CFSOC system.

[0035] Zernike polynomials are commonly used to describe and fit wavefront aberrations caused by atmospheric turbulence in optical systems. These polynomials consist of a continuous sequence of mutually orthogonal polynomials defined on the unit circle, which can transform the distorted wavefront phase into a linear combination of weighted orthogonal polynomials, where each polynomial represents a type of aberration. (26) The constant term Indicates the piston term in the wavefront. and These represent tilt aberrations in the X and Y directions, respectively. These low-order aberrations can be directly corrected by BSU. Higher-order Zernike polynomials have higher fitting accuracy; however, wavefront aberration information affecting CFSOC communication performance is mainly concentrated in modes with lower Zernike aberrations. Therefore, 4th to 15th order Zernike polynomials are selected to model wavefront distortion caused by atmospheric turbulence.

[0036] parameter Used to quantify atmospheric turbulence intensity, by adjusting The values ​​can be used to obtain the Zenich polynomial coefficient distribution corresponding to different turbulence wavefront intensities. Atmospheric turbulence intensity is mainly divided into three categories: weak atmospheric turbulence corresponds to... The value is approximately 2, corresponding to moderate atmospheric turbulence. The value is approximately 10, corresponding to strong atmospheric turbulence. The value is greater than 15. The turbulence intensity can be expressed by combining equation (10) with the previous definition and by using a polynomial fitting method on the turbulent wavefront.

[0037] (27) Here, sigma φ Let RMS represent the root mean square of the residual wavefront phase obtained by Zernike fitting, and its expression is shown in Equation (28): (28) Based on this relationship, the root mean square (RMS) value of the system can be directly estimated using the Zernike coefficients. Using RMS as the core fitness value of the VDNGWO algorithm, the ME and BER of the CFSOC system are derived accordingly.

[0038] Will Set them to 20 and 5 respectively, with the default wavelength set to 635nm. Figure 4 As shown, Figure 4 In the table, (a) represents the Zernike polynomial coefficients generated under strong turbulence conditions, and (b) represents the Zernike polynomial coefficients generated under weak turbulence conditions. First, RMS was used as the algorithm's fitness index for evaluation. To ensure sufficient convergence, the maximum number of iterations was uniformly set to 50. Considering the randomness caused by noise and differences in initial values, 100 independent and repeated experiments were conducted for each turbulence condition. Figure 5 The results show the trend of the root mean square value as a function of the number of iterations under different turbulent conditions (i.e., strong turbulence (a) and weak turbulence (b)).

[0039] The results show that the VDNGWO algorithm can effectively converge within the preset number of iterations under both strong and weak turbulence conditions. In these 100 datasets, an average of 18 iterations are required to reduce the root mean square error (RMS) below 0.2, with a minimum of 11 iterations and a maximum of 24 iterations. Under strong turbulence conditions, an average of 26 iterations are required to reduce the RMS below 0.1, with a minimum of 18 iterations and a maximum of 39 iterations. Under weak turbulence conditions, an average of 6 iterations are required to reduce the RMS below 0.2 (minimum 3, maximum 8 iterations); while an average of 11 iterations are required to reduce it below 0.1 (minimum 7, maximum 15 iterations).

[0040] Independent numerical simulations were conducted for the three proposed improvement strategies to analyze the specific contribution of each strategy to the VDNGWO algorithm. All simulations used root mean square error as the optimization objective, and all other test conditions remained consistent with previous settings to ensure the comparability and reliability of the statistical results. The results for the first two improvement strategies are shown below. Figure 6 As shown, (a) is the position update equation based on velocity under strong turbulence; (b) is the DLH under strong turbulence; (c) is the improved DLH under strong turbulence; (d) is the position update equation based on velocity under weak turbulence; (e) is the DLH under weak turbulence; and (f) is the improved DLH under weak turbulence.

[0041] Figure 6 The velocity-based displacement update equation significantly accelerates the convergence process, but the algorithm is still prone to getting trapped in local optima. The original DLH strategy can partially avoid local optima, but the improvement is limited. When the improved DLH strategy is used alone, it is better than the original DLH in suppressing premature convergence. The improved algorithm based on these two strategies is called the improved GWO algorithm.

[0042] Building upon this, we further propose an optimization strategy based on LPSR—NPSR. To evaluate the efficiency improvement of NPSR compared to LPSR, we conduct tests under different conditions... Under these conditions, the performance of the algorithms was compared for various population reduction strategies. The comparison algorithms included: the enhanced GWO algorithm without population reduction, the LPSR-GWO algorithm with global LPSR application, and the MidLPSR-GWO algorithm with LPSR activated mid-iteration. All other test conditions remained consistent with previous results. Table 1 shows the average running time of each algorithm when the RMS is reduced to 0.1, and the average running time after 50 iterations. The running times of all algorithms were normalized to the running time of the improved GWO algorithm.

[0043] Table 1. Comparison of running times for different population size reduction strategies

[0044] The results show that when At 20, the VDNGWO algorithm improved performance by 21.7% compared to the improved GWO algorithm, LPSR-GWO algorithm, and MidLPSR-GWO algorithm, and by 16.3% and 18.6% compared to the improved GWO algorithm, LPSR-GWO algorithm, and MidLPSR-GWO algorithm, respectively; when When the iteration count is 5, the running time of all algorithms is roughly equivalent, as they can reach the target metric within a relatively small number of iterations, making the population reduction strategy have minimal impact. When the number of iterations is fixed at 50, the VDNGWO algorithm maintains a smaller population size in the later stages, thereby reducing unnecessary computational overhead, and its running time is significantly lower than the other three algorithms—50.9%, 13.8%, and 37.7% shorter than the improved GWO, LPSR-GWO, and MidLPSR-GWO algorithms, respectively. It should be noted that although the LPSR-GWO algorithm also has an advantage in running time, its robustness is lower than other algorithms due to the reduced population diversity caused by the early reduction in population size; in contrast, the VDNGWO algorithm effectively avoids this problem while improving computational efficiency and maintaining good robustness.

[0045] To further evaluate the effectiveness of the VDNGWO algorithm, it was compared with existing SLAO control algorithms. Two representative algorithms successfully applied to wavefront aberration correction—the SPGD algorithm and a hybrid algorithm—were selected, with the unmodified GWO algorithm used as the benchmark. All algorithms were compared and simulated using RMS as the optimization objective. Referring to the parameter settings of existing studies, the gain coefficient λ of both the SPGD and hybrid algorithms was set to 2; the initial temperature of the hybrid algorithm was T=20°C, and the annealing efficiency γ=0.8. To ensure sufficient convergence space and facilitate the demonstration of the convergence process, the maximum number of iterations was set to 500, and each algorithm was run independently 30 times. The results are shown below. Figure 7 As shown, Figure 7 (a) shows the RMS curves of different iteration numbers under various control algorithms in strong turbulence conditions. Figure 7 (b) shows the RMS curves of different iterations for various control algorithms under weak turbulence conditions.

[0046] Simultaneously, using the previously generated Zernike coefficients, original wavefront aberration phase maps and corresponding point spread functions (PSFs) were constructed under different turbulence intensities, such as... Figure 8 As shown, (a) is the original phase diagram under strong turbulence; (b) is the original phase diagram under weak turbulence; (c) is the original point spread function under strong turbulence; and (d) is the original point spread function under weak turbulence. Without correction, the wavefront phase exhibits significant peaks and valleys, and the PSFs show a marked broadening of the focal plane intensity distribution and severe energy scattering, indicating significant wavefront aberrations in the system.

[0047] Figure 9 and Figure 10The evolution of the residual wavefront aberration phase map and the corrected point spread function (PSF) during the iteration process is shown under different turbulence intensities, where (a)~(d) are phase maps and (e)~(h) are point spread functions. Under strong turbulence conditions, the residual aberration decreases significantly after 10 iterations; when the number of iterations increases to 20, the wavefront peak value further decreases, the PSF energy distribution shrinks significantly, and it gradually approaches ideal focusing; when the number of iterations increases to 50, the residual aberration tends to stabilize, indicating that the system performance is close to steady state. Under weak turbulence conditions, the algorithm converges faster and can obtain lower wavefront aberrations with fewer iterations.

[0048] Through the above simulation experiments, wavefront distortion was modeled using Zernike polynomials, and the correction effect of the VDNGWO algorithm was verified under different turbulence intensities. The algorithm was also compared with several traditional algorithms through theoretical analysis and numerical simulation. The results show that the VDNGWO algorithm maintains excellent convergence speed and robustness under strong atmospheric turbulence. This algorithm outperforms other algorithms in terms of the number of iterations and running time required to achieve the predetermined optimization targets. It maintains excellent performance even in complex and variable turbulent environments, effectively correcting wavefront distortion and improving the communication performance of the CFSOC system, thus enhancing the system's robustness.

[0049] It should be noted that other swarm intelligence optimization algorithms (such as Particle Swarm Optimization (PSO), Whale Optimization (WOA), Sparrow Search Algorithm (SSA), Harris Eagle Optimization (HHO), etc.) can also achieve similar wavefront correction effects after introducing the "velocity guidance mechanism" or "nonlinear population reduction strategy" proposed in this invention. All content not described in detail in this specification belongs to prior art known to those skilled in the art. Furthermore, for those skilled in the art, based on the ideas of this invention, there will be changes in specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A coherent free-space optical communication system based on an improved gray wolf algorithm, comprising a wavefront-free adaptive optics system for correcting wavefront distortion, wherein the wavefront-free adaptive optics system includes: A high-speed camera is used to acquire wavefront PSF images and transmit the preprocessed PSF images to the wavefront controller. The wavefront controller is used to identify the wavefront features carried by the PSF image and generate a voltage signal to adjust the wavefront corrector to correct wavefront distortion based on the wavefront features. The wavefront corrector receives the voltage signal and performs wavefront distortion correction, then outputs the corrected optical signal. The wavefront controller is characterized by internally running an improved gray wolf algorithm, which combines velocity-guided updates, dimensionality-learning hunting strategies, and nonlinear population size reduction strategies on the basis of the gray wolf algorithm structure, thereby improving the efficiency and accuracy of wavefront distortion correction applications in wavefront sensorless adaptive optics systems.

2. The coherent free-space optical communication system based on the improved gray wolf algorithm according to claim 1, characterized in that, The improved Grey Wolf algorithm specifically includes the following speed-guided update: ， in, a It is a non-linear decay. A random number taking values ​​in the interval [-1, 1]. For inertial weights, t Indicates the current iteration number. Indicates the maximum number of iterations; By incorporating the global optimal solution during the speed update phase With two randomly selected population solutions and The velocity and position update formulas have been reconstructed to improve local exploration efficiency. The optimized formula is as follows: ， Used to calculate the first step after the introduction of the speed guidance mechanism. i The final position of each individual gray wolf in the next iteration t+1; Among them, the velocity-inertia term Inheriting the gray wolf speed information from the previous time step, express Speed; population memory Guiding the gray wolf towards its historical optimal position, individual memory items By using the random difference between an individual's current position and its own historical position, inter-individual interactions are introduced within the population; It is a cognitive factor. It is a social factor; r 4 and r 5 is a random number in the interval [0,1].

3. The coherent free-space optical communication system based on the improved gray wolf algorithm according to claim 2, characterized in that, The inertial weight , in, It is a random number that takes a value in the interval [0,1]. This is the initial value for the weights, set to 0.9; This is the final value of the weight, taken as 0.1, where e is the base of the natural logarithm.

4. The coherent free-space optical communication system based on the improved gray wolf algorithm according to claim 1, characterized in that, The specific formula for the dimension-learning hunting strategy in the improved gray wolf algorithm is as follows: ， in, X i-GWO (t+1) represents the candidate position calculated by the traditional gray wolf optimization algorithm mechanism; X i-DLH (t+1) represents the candidate positions calculated based on the dimension-based learning hunting strategy; X i-Velocity (t+1) represents the candidate position calculated based on the velocity-guided update strategy; f() is the fitness function.

5. The coherent free-space optical communication system based on the improved gray wolf algorithm according to claim 4, characterized in that, In the dimensional learning hunting strategy, during the early search phase of the algorithm t ≤ 25, individuals with poor fitness and poor performance in the population are reinitialized every three iterations; during the later iteration phase of the algorithm t ≥ 40, candidate positions are finely adjusted by adding noise within 0.5% of the variance of α wolf individuals, and fitness detection is combined to screen for better solutions.

6. The coherent free-space optical communication system based on the improved gray wolf algorithm according to claim 1, characterized in that, The nonlinear population size reduction in the improved gray wolf algorithm specifically refers to: Using a nonlinear sigmoid function instead of a linear function as the benchmark, its mathematical expression is as follows: ， in Indicates the population size for the next iteration; This represents the initial population size, set to 30. This represents the minimum population size required for convergence, which is set to 3. This represents the inflection point, i.e., the number of iterations at which the rate of population size decline reaches its maximum value, and is set to 20. The control parameter representing the rate of decline determines the steepness of the population size change, and its value is 0.4.

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