Signal interference suppression method, device and system

Abstract

The invention discloses a signal interference suppression method, device and system, and belongs to the technical field of wireless communication. According to the method, the optimal phase distribution of the array antenna capable of realizing maximum signal to interference plus noise ratio receiving is searched by adopting the particle swarm algorithm; in a particle swarm initialization process, disturbance is carried out on the basis of current phase distribution obtained by searching a direction vector table by combining a direction vector table and a traditional random generation method, so that an initial value of a position vector of each particle in a particle swarm is constructed; the superiority and diversity of an initial population can be enhanced in a reasonable range, and a good basis is provided for global optimal solution search of a subsequent particle swarm algorithm; the method does not need to depend on complete channel information, and can still realize effective interference suppression even in a scene of low-altitude communication and moving of a target signal transmitting end.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication, and more particularly, relates to a signal interference suppression method, apparatus and system. Background Art

[0002] Signal interference has always been a very important topic in the field of wireless communication, and the low-altitude communication scenario poses higher requirements on the anti-interference performance of the communication system. Generally, if the interference signal and the useful signal occupy the same spectrum, common frequency-domain filters cannot effectively suppress the interference. To suppress the interference, the common current idea is to perform adaptive beamforming on the receiving antenna array, that is, a spatial filter, to enhance the useful signal of interest while suppressing interference and noise signals from multiple directions.

[0003] Currently, the research on beamforming algorithms mainly focuses on the beamforming strategy of discrete phase control to optimize the system performance. This method is relatively simple and easy to implement. However, traditional beamforming methods often rely on complete channel information. In the scenario of low-altitude communication, when the target signal transmitter moves, the channel information will change, and it is difficult to ensure the real-time performance and accuracy of channel estimation, resulting in poor beamforming effect and inability to effectively suppress interference. Summary of the Invention

[0004] Aiming at the above defects or improvement requirements of the prior art, the present invention provides a signal interference suppression method, apparatus and system to solve the technical problem that the prior art cannot effectively suppress interference.

[0005] To achieve the above object, in a first aspect, the present invention provides a signal interference suppression method for a receiving end; the receiving end includes: an array antenna; the array antenna includes: N antenna elements; N≥2;

[0006] The above signal interference suppression method includes:

[0007] S1. Based on the current relative position pos between the target signal transmitter and the receiving end, query the array antenna phase distribution corresponding to the relative position with the smallest difference from pos in the direction vector table as the current phase distribution of the array antenna; the direction vector table includes: multiple pre-collected relative positions between the target signal transmitter and the receiving end, and the corresponding array antenna phase distributions that make the main lobe of the array antenna point to the target signal transmitter;

[0008] S2. For the L phase vectors corresponding to the L phase distributions obtained by perturbing the current phase distribution L times respectively, use them one by one as the initial position vectors of the L particles in the particle swarm algorithm; for the L step vectors corresponding to the randomly generated L phase perturbation step distributions, use them one by one as the initial velocity vectors of the L particles; L≥1; the r-th element in the phase vector is where j is the imaginary symbol; is the r-th phase in the corresponding phase distribution; the r-th element in the step vector is which is the r-th phase perturbation step in the corresponding phase perturbation step distribution; r = 1, 2, …, N;

[0009] S3. Use the signal-to-interference-plus-noise ratio (SINR) of the array antenna when the phase distribution of the array antenna is adjusted to the phase distribution corresponding to the position vector of the particle as the fitness value of the particle. Then, use the particle swarm algorithm to search for the optimal position vector, and take the phase distribution corresponding to the optimal position vector as the optimal phase distribution of the array antenna;

[0010] S4. Adjust the phase distribution of the array antenna to its optimal phase distribution to achieve maximum SINR reception.

[0011] Further preferably, belongs to the range belongs to the range where is half of the main lobe width of the array antenna.

[0012] Further preferably, the process of using the particle swarm algorithm to search for the optimal position vector includes multiple iterations; in each iteration, based on the current fitness value of each particle in the particle swarm, obtain the current individual optimal solution of the particle and the current global optimal solution of the particle swarm. When the iteration cutoff condition is not reached, update the individual learning factor, social learning factor, the position vector and velocity vector of each particle in the particle swarm, and perform the next iteration; when the iteration cutoff condition is reached, take the current global optimal solution of the particle swarm as the optimal position vector;

[0013] where the iteration cutoff condition includes that the number of iterations reaches the preset maximum number of iterations or the current global optimal solution of the particle swarm is greater than the preset threshold;

[0014] The current individual optimal solution of the particle is the position vector corresponding to the maximum value of the fitness value of the particle during the process from the first iteration to the current iteration;

[0015] The current global optimal solution of the particle swarm is the position vector corresponding to the maximum value of the fitness values of all particles in the particle swarm during the process from the first iteration to the current iteration.

[0016] Further preferably, the update formulas for the individual learning factor and the social learning factor are functions of the iteration number n; the individual learning factor decreases as the iteration number n increases, the social learning factor increases as the iteration number n increases, and the individual learning factor and the social learning factor vary between a preset minimum learning factor and a preset maximum learning factor.

[0017] Further preferably, the individual learning factor c1(n) and the social learning factor c2(n) at the n-th iteration are respectively:

[0018]

[0019] where c max and c min are the preset maximum learning factor and the preset minimum learning factor respectively; K is the preset maximum number of iterations; and are both functions of the iteration number n, both increase as the iteration number n increases, and

[0020] Further preferably, and are the same.

[0021] Further preferably, the velocity vector and the position vector of the i-th particle in the particle swarm are updated in the following manner:

[0022] Using the update formula for the velocity vector of the i-th particle in the particle swarm v i (n + 1) = w·v i (n) + c1(n)·r1·(pBest i (n(-x i (n)) + c2(n)·r2·(gBest(n) - x i (n)), calculate the velocity vector v i (n + 1) of the i-th particle at the (n + 1)-th iteration; judge the phase perturbation step size corresponding to each element in v i (n + 1), and set the phase perturbation step size greater than to Set the phase perturbation step size less than to

[0023] Using the update formula for the position vector of the i-th particle in the particle swarm x i (n + 1) = x i (n) + v i (n + 1), calculate the position vector x i (n + 1) of the i-th particle at the (n + 1)-th iteration; for xi Judge the phase corresponding to each element in (n + 1), and set the phase perturbation step greater than as Set the phase perturbation step less than as

[0024] where, v i (n) is the velocity vector of the i-th particle at the n-th iteration; w is a preset inertia weight; c1(n) is the individual learning factor at the n-th iteration; c2(n) is the social learning factor at the n-th iteration; r1 and r2 are both random numbers; x i (n) is the position vector of the i-th particle at the n-th iteration; pBest i (n) is the current individual optimal solution of the i-th particle; gBest(n) is the current global optimal solution of the particle swarm.

[0025] In a second aspect, the present invention provides a signal interference suppression device, including: a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it executes the signal interference suppression method provided in the first aspect of the present invention.

[0026] In a third aspect, the present invention provides a signal receiving device, including: an array antenna; the array antenna includes: the signal interference suppression device provided in the second aspect of the present invention and N antenna units; N ≥ 2.

[0027] In a fourth aspect, the present invention provides a communication system, including: a target signal transmitting end and the signal receiving device provided in the third aspect of the present invention;

[0028] wherein, the target signal transmitting end is used to transmit a signal to the signal receiving device.

[0029] In a fifth aspect, the present invention further provides a computer-readable storage medium, the computer-readable storage medium includes a stored computer program, wherein, when the computer program is run by a processor, it controls the device where the storage medium is located to execute the method provided in the first aspect of the present invention.

[0030] In a sixth aspect, the invention further provides a computer program product, including computer programs / instructions, and when the computer programs / instructions are executed by a processor, they implement the method provided in the first aspect of the present invention.

[0031] Generally speaking, through the above technical solutions conceived by the present invention, the following beneficial effects can be achieved:

[0032] 1. The present invention provides a signal interference suppression method, which uses the particle swarm algorithm to find the optimal phase distribution of the array antenna that can achieve the maximum signal-to-interference-plus-noise ratio (SINR) reception; during the initialization process of the particle swarm, by combining the method based on the direction vector table and the traditional random generation method, perturbations are made on the basis of the current phase distribution obtained from the lookup direction vector table to construct the initial values of the position vectors of each particle in the particle swarm, which can enhance the superiority and diversity of the initial population within a reasonable range and provide a good basis for the subsequent global optimal solution search of the particle swarm algorithm; the present invention does not need to rely on complete channel information, and can still achieve effective interference suppression even in the scenario of low-altitude communication and the movement of the target signal transmitter.

[0033] 2. Further, the signal interference suppression method provided by the present invention belongs to the range is half of the main lobe width of the array antenna. By realizing a small random range offset of the current phase distribution of the array antenna, the range of the phases involved in the position vectors of each particle in the initial particle swarm is restricted, which can, on the basis of enhancing the superiority and diversity of the initial population, avoid large-angle offsets of the main lobe of the beam, and further improve the signal-to-interference-plus-noise ratio on the premise of stable connection; at the same time, belongs to the range to restrict the probability distribution range of the velocity vectors of each particle in the initial particle swarm, which can effectively avoid the problem of search instability caused by too large velocity amplitude while ensuring the diversity of the particle swarm to enhance the global exploration ability of the solution space. Based on this, the present invention further improves the interference suppression effect.

[0034] 3. Further, the signal interference suppression method provided by the present invention updates the individual learning factor and the social learning factor during each iteration process. The update formulas c1(n) and c2(n) of the individual learning factor and the social learning factor are both functions with the iteration number n as a variable, and ensure that the individual learning factor and the social learning factor change between their minimum and maximum ranges. At the same time, in the initial stage of iteration, the individual learning factor is large and the social learning factor is small, and the particles pay more attention to individual cognition. While in the later stage of iteration, the individual learning factor is small and the social learning factor is large, and the particles tend to learn from the population more, which can improve the convergence speed of iteration, further improve the signal interference suppression speed, has strong real-time performance, and is more suitable for the scenario of low-altitude communication and the movement of the target signal transmitter.

[0035] 4. Further, the signal interference suppression method provided by the present invention also ensures that during the update process of the velocity vector and the position vector of the particle, each element of the updated velocity vector v i (n + 1) corresponds to a phase perturbation step within the range of the range, and the updated position vector xi (n + 1) The phase corresponding to each element is within the range, which can also avoid large-angle deviation of the main lobe of the beam during the update process and the problem of search instability caused by excessive speed amplitude, further improving the interference suppression effect. Description of the Drawings

[0036] Figure 1 It is a flowchart of a signal interference suppression method provided by an embodiment of the present invention;

[0037] Figure 2 It is a schematic diagram of a downlink system model provided by an embodiment of the present invention;

[0038] Figure 3 It is a schematic diagram of an array antenna provided by an embodiment of the present invention;

[0039] Figure 4 It is a simulation result of the change of fitness value during the optimization process of four algorithms, namely discrete PSO, continuous PSO, PSO with only initial position optimization, and PSO with initial position and velocity parameter optimization, during the signal interference suppression process provided by an embodiment of the present invention. Detailed Embodiments

[0040] In order to make the objectives, technical solutions and advantages 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 only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0041] To achieve the above objective, in a first aspect, the present invention provides a signal interference suppression method for a receiving end; the receiving end includes: an array antenna; the array antenna includes: N antenna elements; N ≥ 2;

[0042] As Figure 1 shown, the above signal interference suppression method includes:

[0043] S1. Based on the current relative position pos between the target signal transmitter and the receiving end, query the array antenna phase distribution corresponding to the relative position with the smallest difference from pos in the direction vector table as the current phase distribution of the array antenna; the content stored in the direction vector table is the main lobe direction and the corresponding phase distribution, including: multiple pre-collected relative positions between the target signal transmitter and the receiving end, and the corresponding array antenna phase distributions that make the main lobe of the array antenna point to the target signal transmitter; among them, the array antenna phase distribution includes: the phases of the N antenna elements in the array antenna.

[0044] It should be noted that the relative position pos can be represented by pitch angle and azimuth angle, or directly by the relative position in the three-dimensional Cartesian coordinate system, which is not limited here.

[0045] There are various methods to obtain the current relative position between the target signal transmitter and the receiver. It can be obtained based on the positioning signal sent by the target signal transmitter (such as GPS signal, obtained by the GPS module on the target transmitter and then sent to the receiver, using a different frequency band from other interfering base stations and the target transmitter, so there will be no great interference), and it can also use methods such as passive radio positioning based on TDOA / FDOA and long-distance target positioning based on computer vision at the receiver, which is not limited here.

[0046] The phase distribution of the array antenna that makes the main lobe of the array antenna point to the target signal transmitter in the direction vector table can be calculated by the beamforming algorithm based on the relative position between the corresponding target signal transmitter and the receiver.

[0047] S2. The phase vectors corresponding to the L phase distributions obtained after perturbing the current phase distribution L times are used as the initial position vectors of the L particles in the particle swarm algorithm one by one; the step vectors corresponding to the randomly generated L phase perturbation step distributions are used as the initial velocity vectors of the L particles one by one; L is the number of particles in the particle swarm; L≥1; the r-th element in the phase vector is j is the imaginary symbol; is the r-th phase in the corresponding phase distribution; the r-th element in the step vector is is the r-th phase perturbation step in the corresponding phase perturbation step distribution; r = 1, 2,..., N;

[0048] Considering the above-mentioned way of randomly generating the initial positions of the particles, although it can ensure the diversity of the population and the global search ability, it is relatively blind and the convergence speed is slow. To solve the above problems, preferably, in an alternative implementation, Belonging to the range Belonging to the range Among them, is the maximum phase perturbation step, determined by the main lobe width of the array antenna, preferably half of the main lobe width of the array antenna.

[0049] The above design of the initial values of the position vectors greatly enhances the superiority of the initial positions of the population, providing a good foundation for subsequent optimization. The above design of the initial values of the velocity vectors effectively avoids the search instability phenomenon caused by too large velocity amplitude while ensuring the diversity of the particle swarm to enhance the global exploration ability of the solution space by restricting the probability distribution range of the initial velocity.

[0050] The present invention has made improvements in the particle swarm initialization stage. By combining the method based on the direction vector table and the traditional random generation method, a small random offset is performed on the basis of the current phase distribution obtained from the lookup direction vector table to construct the initial value of the position vector of each particle in the particle swarm. On the basis of enhancing the superiority and diversity of the initial population, large-angle offsets of the main lobe of the beam are avoided, and on the premise of stable connection, the signal-to-interference-plus-noise ratio is further improved, and the anti-interference ability of the system is enhanced.

[0051] S3. Take the signal-to-interference-plus-noise ratio of the array antenna when the phase distribution of the array antenna is adjusted to the phase distribution corresponding to the position vector of the particle as the fitness value of the particle, use the particle swarm algorithm to search for the optimal position vector, and take the phase distribution corresponding to the optimal position vector as the optimal phase distribution of the array antenna;

[0052] It should be noted that there are multiple specific implementation methods for the particle swarm algorithm, which are not limited here. No matter which method, the process of searching for the optimal position vector using the particle swarm algorithm includes multiple iterations. In each iteration, the position vector and velocity vector of each particle in the particle swarm will be updated. The difference lies in whether other parameters in the update formulas of the position vector and velocity vector of the particle, such as the learning factor and inertia weight, are updated, and the update method. In some alternative embodiments, neither the learning factor nor the inertia weight in the update formulas of the position vector and velocity vector of the particle is updated. In some alternative embodiments, both the learning factor and the inertia weight in the update formulas of the position vector and velocity vector of the particle are updated.

[0053] Preferably, in an alternative embodiment, the learning factor in the update formulas of the position vector and velocity vector of the particle is updated, and the inertia weight is not updated. Specifically, in each iteration, based on the current fitness value of each particle in the particle swarm, obtain the current individual optimal solution of the particle and the current global optimal solution of the particle swarm. When the iteration cutoff condition is not reached, update the individual learning factor, social learning factor, the position vector and velocity vector of each particle in the particle swarm, and perform the next iteration; when the iteration cutoff condition is reached, take the current global optimal solution of the particle swarm as the optimal position vector;

[0054] Among them, the iteration cutoff condition includes that the number of iterations reaches the preset maximum number of iterations or the current global optimal solution of the particle swarm is greater than the preset threshold;

[0055] The current individual optimal solution of the particle is the position vector corresponding to the maximum value of the fitness value of the particle during the process from the first iteration to the current iteration;

[0056] The current global optimal solution of the particle swarm is the position vector corresponding to the maximum value of the fitness values of all particles in the particle swarm during the process from the first iteration to the current iteration.

[0057] In an alternative embodiment, the update formula for the velocity vector of the i-th particle in the particle swarm is:

[0058] v i (n + 1) = w·v i (n) + c1(n)·r1·(pBest i (n) - x i (n)) + c2(n)·r2·(gBest(n) - x i (n)

[0059] The update formula for the position vector of the i-th particle in the particle swarm is:

[0060] x i (n + 1) = x i (n) + v i (n + 1)

[0061] Where, v i (n) is the velocity vector of the i-th particle at the n-th iteration; w is a preset inertia weight; c1(n) is the individual learning factor at the n-th iteration; c2(n) is the social learning factor at the n-th iteration; r1 and r2 are both random numbers; x i (n) is the position vector of the i-th particle at the n-th iteration; pBest i (n) is the current individual optimal solution of the i-th particle; gBest(n) is the current global optimal solution of the particle swarm.

[0062] Preferably, in an alternative embodiment, the velocity vector and position vector of the i-th particle in the particle swarm are updated as follows:

[0063] Using the update formula v i (n + 1) = w·v i (n) + c1(n)·r1·(pBest i (n) - x i (n)) + c2(n)·r2·(gBest(n) - x i (n)) of the velocity vector of the i-th particle in the particle swarm, calculate the velocity vector v i (n + 1) of the i-th particle at the (n + 1)-th iteration; judge the phase perturbation step size corresponding to each element in v i (n + 1), and set the phase perturbation step size greater than to Set the phase perturbation step size less than to

[0064] Use the update formula x of the position vector of the i-th particle in the particle swarm i (n + 1) = x i (n) + v i (n + 1) to calculate the position vector x of the i-th particle at the (n + 1)-th iteration i (n + 1); judge the phase corresponding to each element in x i (n + 1), and set the phase perturbation step size greater than to Set the phase perturbation step size less than to

[0065] It should be noted that by fusing the mechanisms of individual particle cognition and social cooperation to drive speed update, its core control parameters include the inertia weight w, the individual cognition coefficient, and the social cooperation coefficient.

[0066] In an alternative embodiment, the update formulas of the individual learning factor and the social learning factor are functions of the iteration number n; the individual learning factor decreases as the iteration number n increases, the social learning factor increases as the iteration number n increases, and the individual learning factor and the social learning factor vary between a preset minimum learning factor and a preset maximum learning factor.

[0067] Specifically, the individual learning factor c1(n) and the social learning factor c2(n) at the n-th iteration are respectively:

[0068]

[0069] where c max and c min are the preset maximum learning factor and the preset minimum learning factor respectively; K is the preset maximum number of iterations; and are both functions of the iteration number n, and both increase as the iteration number n increases, and

[0070] It should be noted that f1(·) can be a linear function, an arctangent function, an exponential function, etc., and is not limited here. In an alternative embodiment, or or

[0071] f2(·) can be a linear function, an arctangent function, an exponential function, etc., and is not limited here. In an alternative embodiment, or or

[0072] It should be noted that f1(·) and f2(·) can be the same; they can also be different; preferably, f1(·) and f2(·) are the same.

[0073] In an alternative embodiment 1, the individual learning factor c1(n) and the social learning factor c2(n) at the n-th iteration are respectively:

[0074]

[0075] In an alternative embodiment 2, the individual learning factor c1(n) and the social learning factor c2(n) at the n-th iteration are respectively:

[0076]

[0077] In an alternative embodiment 3, the individual learning factor c1(n) and the social learning factor c2(n) at the n-th iteration are respectively:

[0078]

[0079] The present invention significantly improves the search ability and convergence speed of the particle swarm optimization algorithm by dynamically adjusting the individual and population learning factors.

[0080] S4. Adjust the phase distribution of the array antenna to its optimal phase distribution to achieve maximum signal-to-interference-plus-noise ratio (SINR) reception.

[0081] The present invention proposes a signal interference suppression method based on the particle swarm optimization algorithm, uses the SINR at the receiving end as the fitness function, adjusts the phase distribution of the receiving array, and achieves maximum SINR reception. Based on the position information beamforming scheme, the present invention further optimizes the phase distribution of the array, realizes maximum SINR reception, enhances the anti-interference ability of the system, and improves the system performance in a dynamic environment.

[0082] It should be noted that the present invention is applicable to high-altitude communication scenarios, and is also applicable to low-altitude communication scenarios, and can particularly solve the technical problem of ineffective interference suppression in the scenario of low-altitude communication with a moving target signal transmitter.

[0083] To further illustrate the signal interference suppression method provided by the present invention, the following is a detailed description in combination with a specific embodiment:

[0084] Taking the application of the signal interference suppression method to a downlink system as an example, where the target signal transmitter is the target unmanned aerial vehicle (UAV), and the interference sources are interference base stations distributed at different positions. The target UAV plus the interference base stations can be regarded as a total of M transmitters; the receiver includes: an array antenna, which is a uniform planar array (UPA) antenna with N elements in this embodiment, specifically including: N antenna elements distributed in an array; N≥2;

[0085] In this embodiment, the following three-dimensional Cartesian coordinate system is established, and the origin of the three-dimensional Cartesian coordinate system is located at the lower left corner of the planar array antenna. The channel is a simple channel, that is, there is only one unobstructed line-of-sight (LOS) path between the base station and the antenna, and the Doppler effect is not considered. In addition, the transmitter only knows the azimuth of the target UAV relative to the array. The system model is as Figure 2 shown.

[0086] The calculation method of the signal y received by the receiver is as follows:

[0087] y = xHg T + n0

[0088] where, is the signal vector transmitted by M transmitters. is the channel matrix. is the beamforming weight vector, and n0 is the additive white Gaussian noise with power σ 2

[0089] The row vector of the channel matrix H that is, the channel from the m-th transmitter to the receiver can be modeled as:

[0090]

[0091] where, f is the frequency of the signal carrier; is the complex amplitude when the m-th base station reaches the receiver, is the propagation delay from the m-th base station to the receiver, and the calculation formula is

[0092]

[0093] where, represents the position vector of the m-th transmitter; d = [d1, d2, ……, d N is the position vector of each unit of the UPA array, represents the distance vector from the array unit to the m-th transmitter; c is the speed of light.

[0094] A coordinate system is established for the receiving array, as Figure 3 shown, then d n is:

[0095] ​

[0096] a m (φ m ,θ m ) is the steering vector from the m-th transmitter to the receiver, and can be expressed as:

[0097]

[0098] p m is the spherical coordinate unit direction vector pointing from the receiver to the m-th transmitter, and can be expressed as:

[0099]

[0100] The signal-to-interference-plus-noise ratio (SINR) at the final receiver is:

[0101]

[0102] Among them, f is the serial number of the target UAV, and m≠f is the serial number of the interfering base station. The magnitude of the SINR value can represent the interference suppression ability of the system.

[0103] The interference suppression method in this embodiment specifically includes the following process:

[0104] S1. Based on the current relative position pos between the target signal transmitter and the receiver, query the array antenna phase distribution corresponding to the relative position with the smallest difference from pos in the direction vector table as the current phase distribution of the array antenna; the direction vector table includes: multiple pre-collected relative positions between the target signal transmitter and the receiver, and the corresponding array antenna phase distributions that make the main lobe of the array antenna point to the target signal transmitter;

[0105] S2. Use the phase vectors corresponding to the L phase distributions obtained by respectively perturbing the current phase distribution L times as the initial position vectors of the L particles in the particle swarm algorithm (PSO); use the step vectors corresponding to the randomly generated L phase perturbation step distributions as the initial velocity vectors of the L particles; L≥1; the r-th element in the phase vector is j is the imaginary symbol; is the r-th phase in the corresponding phase distribution; the r-th element in the step vector is is the r-th phase perturbation step in the corresponding phase perturbation step distribution; r = 1, 2,..., N;

[0106] In this embodiment, belongs to the range belongs to the range Among them, is half of the main lobe width HPBW of the array antenna. The array antenna in this embodiment is a uniform planar array (UPA) antenna with N elements,

[0107] Through the above limitations, each phase in the L phase distributions obtained after perturbing the current phase distribution L times belongs to the range such that each phase perturbation step size in the randomly generated L-phase perturbation step size distributions belongs to the range Through the above design, the initial position vectors of each particle are randomly distributed near the current phase distribution of the array antenna.

[0108] S3. Take the signal-to-interference-plus-noise ratio (SINR) of the array antenna when the phase distribution of the array antenna is adjusted to the phase distribution corresponding to the position vector of the particle as the fitness value of the particle, and use the particle swarm optimization (PSO) algorithm to search for the optimal position vector, and take the phase distribution corresponding to the optimal position vector as the optimal phase distribution of the array antenna;

[0109] Denote the position vector and velocity vector of the i-th particle in the particle swarm at the n-th iteration as x i (n) and v i (n). When n = 1, x i (n) and v i (n) are the initial values of the position vector and velocity vector of the i-th particle.

[0110] represents the phase distribution situation of the array antenna, while represents the phase change situation of the array antenna. The range of the position is (0, 2π], and the fitness is the signal-to-interference-plus-noise ratio of the array antenna.

[0111] In this embodiment, S3 specifically includes:

[0112] S31. Let n = 1;

[0113] S32. Traverse the L particles in the particle swarm, obtain the current fitness value of each particle, and record the maximum value as SINR gmax (n); where the current fitness value of the i-th particle is SINR i (n), that is, the signal-to-interference-plus-noise ratio of the array antenna when the phase distribution of the array antenna is adjusted to the phase distribution corresponding to x i (n); i = 1, 2,..., L; when n = 1, take the phase distribution corresponding to x i (n) as the current individual optimal solution pBest i (n) of the i-th particle; take SINR gmax(n) corresponds to the position vector as the current global optimal solution gBest(n); when n≥2, SINR i (n) and pBest i (n - 1), the position vector of the particle corresponding to the larger fitness value is used as the current personal optimal solution pgBest i (n) of the i-th particle; the position vector of the particle corresponding to the larger value between SINR gmax (n) and gBest(n - 1) is used as the current global optimal solution gBest(n);

[0114] It should be noted that in the particle swarm optimization algorithm, the personal optimal solution refers to the position vector with the highest fitness that each particle itself appears in the iterative process. Each particle compares the current fitness with the fitness corresponding to the personal optimal solution obtained by the individual in the previous iterative process. If the current fitness is greater than the fitness corresponding to the personal optimal solution, the personal optimal solution is updated to the current position vector, and at the same time, the fitness corresponding to the personal optimal solution is updated to the current fitness. Otherwise, the personal optimal solution is not updated.

[0115] In the particle swarm optimization algorithm, the global optimal solution refers to the position with the highest fitness that appears in the entire population during the iterative process. Each particle compares the current fitness with the fitness corresponding to the global optimal solution. If the current fitness is greater than the fitness corresponding to the global optimal solution, the global optimal solution is updated to the current position, and at the same time, the fitness corresponding to the global optimal solution is updated to the current fitness. Otherwise, the global optimal solution is not updated.

[0116] S33. Determine whether the iteration cut-off condition is reached. If so, go to S34; otherwise, update the individual learning factor, social learning factor, the position vector and velocity vector of each particle in the particle swarm, let n=n + 1, and go to S32;

[0117] Since the initial position already has sufficient superiority, the inertia weight w should be appropriately reduced to achieve a more refined search; while the individual cognitive coefficient and social cooperation coefficient determine the proportion of the particle learning from personal and group experience, and need to be dynamically adjusted according to the number of iterations to ensure a fast enough convergence speed. Therefore, both the individual learning factor and the social learning factor are functions of the iteration number n.

[0118] The individual learning factor c1(n) and social learning factor c2(n) at the n-th iteration are respectively:

[0119]

[0120] Among them, c max and c minThey are the preset maximum learning factor and the preset minimum learning factor respectively; K is the preset maximum number of iterations.

[0121] Based on the above design, it is ensured that the individual cognitive coefficient and the social learning factor vary between [c min , c max . At the same time, in the initial stage of iteration (the individual cognitive coefficient is large and the social learning factor is small), the particle pays more attention to individual cognition, while in the later stage of iteration (the individual cognitive coefficient is small and the social learning factor is large), the particle is more inclined to learn from the population.

[0122] Using the update formula of the velocity vector of the i-th particle in the particle swarm v i (n + 1) = w·v i (n) + c1(n)·r1·(pBest i (n) - x i (n)) + c2(n)·r2·(gBest(n) - x i (n)), calculate the velocity vector v i (n + 1) of the i-th particle at the (n + 1)-th iteration; judge the phase perturbation step size corresponding to each element in v i (n) + 1), and set the phase perturbation step size greater than to Set the phase perturbation step size less than to

[0123] Using the update formula of the position vector of the i-th particle in the particle swarm x i (n + 1) = x i (n) + v i (n + 1), calculate the position vector x i (n + 1) of the i-th particle at the (n + 1)-th iteration; judge the phase corresponding to each element in x i (n + 1), and set the phase perturbation step size greater than to Set the phase perturbation step size less than to

[0124] Among them, v i (n) is the velocity vector of the i-th particle at the n-th iteration; w is the preset inertia weight, indicating the dependence degree of the current velocity vector on the previous velocity vector; r1 and r2 are both random numbers in the interval [0, 1]; x i (n) is the position vector of the i-th particle at the n-th iteration; pBest i (n) is the current individual optimal solution of the i-th particle; gBest(n) is the current global optimal solution of the particle swarm.

[0125] The particles adjust their positions according to the velocities obtained by the current update. To ensure the validity of the positions, it is usually necessary to constrain the positions of the particles.

[0126] S34. Use the current global optimal solution pBest(n) of the particle swarm as the optimal position vector.

[0127] Among them, the iteration termination conditions include that the iteration number n reaches the preset maximum iteration number or the current global optimal solution gBest(n) of the particle swarm is greater than the preset threshold. In this embodiment, the preset maximum iteration number and the preset threshold are respectively set to 500 and 10 dB.

[0128] This kind of iteration termination condition avoids the risk of algorithm divergence through rigid iteration constraints, ensures the efficient utilization of computing resources, and at the same time, the threshold-triggered termination strategy can avoid invalid iterations, significantly improving the search efficiency.

[0129] S4. Adjust the phase distribution of the array antenna to its optimal phase distribution to achieve maximum signal-to-interference-plus-noise ratio reception.

[0130] To further verify the signal interference suppression method provided by the present invention, the corresponding simulation parameters are set as shown in Table 1.

[0131] Table 1

[0132]

[0133] Figure 4 Shows the simulation results of the fitness value changes of four algorithms during the signal interference suppression process, namely basic PSO (including discrete PSO and continuous PSO), improved PSO (PSO with only initial position optimization and PSO with initial position and velocity parameter optimization). Analyze from the convergence speed and the magnitude of the fitness.

[0134] Discrete PSO: Since the solution space of discrete PSO is much smaller (only 1-bit quantization), the convergence speed is the fastest, but the quantization accuracy is low, and the optimal solution cannot be accurately found, and the best fitness is the lowest.

[0135] Continuous PSO: Since the solution space of continuous PSO is much larger than that of discrete PSO, the convergence speed is relatively slow, but the phase can be continuously adjusted, and the optimal solution can be found more accurately, and the best fitness is much higher than that of discrete PSO.

[0136] PSO with initial position optimization: Since the initial position has superiority compared with continuous PSO, the convergence speed and the best fitness are slightly higher than those of continuous PSO.

[0137] Initial position optimization and velocity parameter optimization PSO: Due to the optimization of the velocity parameters, the convergence speed is much higher than that of the other three algorithms, and the best fitness is also the highest.

[0138] In a second aspect, the present invention provides a signal interference suppression device, comprising: a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it executes the signal interference suppression method provided in the first aspect of the present invention.

[0139] The related technical solutions are the same as those of the signal interference suppression method provided in the first aspect of the present invention and will not be limited here.

[0140] In a third aspect, the present invention provides a signal receiving device, comprising: an array antenna; the array antenna includes: the signal interference suppression device provided in the second aspect of the present invention and N antenna units; N≥2.

[0141] The related technical solutions are the same as those of the signal interference suppression device provided in the second aspect of the present invention and will not be limited here.

[0142] In a fourth aspect, the present invention provides a communication system, comprising: a target signal transmitting end and the signal receiving device provided in the third aspect of the present invention;

[0143] wherein, the target signal transmitting end is used to transmit a signal to the signal receiving device.

[0144] The related technical solutions are the same as those of the signal receiving device provided in the third aspect of the present invention and will not be limited here.

[0145] In a fifth aspect, the present invention further provides a computer-readable storage medium, the computer-readable storage medium includes a stored computer program, wherein when the computer program is run by a processor, it controls the device where the storage medium is located to execute the method provided in the first aspect of the present invention.

[0146] The related technical solutions are the same as those of the method provided in the first aspect of the present invention and will not be limited here.

[0147] In a sixth aspect, the invention further provides a computer program product, comprising computer programs / instructions, and when the computer programs / instructions are executed by a processor, they implement the method provided in the first aspect of the present invention.

[0148] The related technical solutions are the same as those of the method provided in the first aspect of the present invention and will not be limited here.

[0149] Those skilled in the art can easily understand that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A signal interference suppression method, characterized in that, For the receiving end; The receiving end includes: an array antenna; the array antenna includes: N antenna elements; N≥2; The signal interference suppression method includes: S1. Based on the current relative position pos between the target signal transmitting end and the receiving end, query the array antenna phase distribution corresponding to the relative position with the smallest difference from pos in the direction vector table as the current phase distribution of the array antenna; the direction vector table includes: multiple pre-acquired relative positions between the target signal transmitting end and the receiving end, and the corresponding array antenna phase distributions that make the main lobe of the array antenna point to the target signal transmitting end; S2. Use the phase vectors corresponding to the L phase distributions obtained by perturbing the current phase distribution L times one by one as the initial values of the position vectors of the L particles in the particle swarm algorithm; use the step vectors corresponding to the L randomly generated phase perturbation step distributions one by one as the initial values of the velocity vectors of the L particles; L≥1; the r-th element in the phase vector is j is the imaginary symbol; is the r-th phase in the corresponding phase distribution; the r-th element in the step vector is is the r-th phase perturbation step in the corresponding phase perturbation step distribution; r = 1, 2,..., N; S3. Use the signal-to-interference-plus-noise ratio of the array antenna when the phase distribution of the array antenna is adjusted to the phase distribution corresponding to the position vector of the particle as the fitness value of the particle, and use the particle swarm algorithm to search for the optimal position vector, and use the phase distribution corresponding to the optimal position vector as the optimal phase distribution of the array antenna; S4. Adjust the phase distribution of the array antenna to the optimal phase distribution to achieve maximum signal-to-interference-plus-noise ratio reception.

2. The signal interference suppression method according to claim 1, wherein belong to the range belong to the range wherein is half of the main lobe width of the array antenna.

3. The signal interference suppression method according to claim 1 or 2, wherein The process of using the particle swarm algorithm to search for the optimal position vector includes multiple iterations; in each iteration, based on the current fitness value of each particle in the particle swarm, obtain the current individual optimal solution of the particle and the current global optimal solution of the particle swarm. When the iteration cutoff condition is not reached, update the individual learning factor, the social learning factor, the position vector and the velocity vector of each particle in the particle swarm, and perform the next iteration; when the iteration cutoff condition is reached, use the current global optimal solution of the particle swarm as the optimal position vector; Wherein, the iteration cutoff condition includes that the number of iterations reaches the preset maximum number of iterations or the current global optimal solution of the particle swarm is greater than the preset threshold; The current individual optimal solution of the particle is the position vector corresponding to the maximum value of the fitness value of the particle during the process from the first iteration to the current iteration; The current global optimal solution of the particle swarm is the position vector corresponding to the maximum value of the fitness values of all particles in the particle swarm during the process from the first iteration to the current iteration.

4. The signal interference suppression method according to claim 3, wherein The update formulas of the individual learning factor and the social learning factor are functions with the iteration number n as a variable; the individual learning factor decreases as the iteration number n increases, the social learning factor increases as the iteration number n increases, and the individual learning factor and the social learning factor vary between the preset minimum learning factor and the preset maximum learning factor.

5. The signal interference suppression method according to claim 4, wherein The individual learning factor c1(n) and the social learning factor c2(n) in the nth iteration are respectively: where c max and c min are the preset maximum learning factor and the preset minimum learning factor respectively; K is the preset maximum number of iterations; and are both functions with the iteration number n as a variable, both increase as the iteration number n increases, and 6. The signal interference suppression method according to claim 5, characterized in that and are the same.

7. The signal interference suppression method according to claim 3, wherein The velocity vector and the position vector of the i-th particle in the particle swarm are updated in the following manner: Using the update formula for the velocity vector of the $i$-th particle in the particle swarm $v$ i (n + 1)=w·v i (n)+c1(n)·r1·(pBest i (n)-x i (n))+c2(n)·r2·(gBest(n)-x i (n)), the velocity vector $v$ of the $i$-th particle at the $(n + 1)$-th iteration is calculated as $v$ i (n + 1); and for each element in $v$ i (n + 1), the corresponding phase perturbation step size is judged, and the phase perturbation step size greater than is set to The phase perturbation step size less than is set to Using the update formula x of the position vector of the i-th particle in the particle swarm i (n + 1) = x i (n) + v i (n + 1), the position vector x of the i-th particle at the (n + 1)-th iteration is calculated i (n + 1); and the phase corresponding to each element in x i (n + 1) is judged, and the phase perturbation step size greater than is set to The phase perturbation step size less than is set to where, v i (n) is the velocity vector of the i-th particle at the n-th iteration; w is a preset inertia weight; c1(n) is the individual learning factor at the n-th iteration; c2(n) is the social learning factor at the n-th iteration; r1 and r2 are both random numbers; x i (n) is the position vector of the i-th particle at the n-th iteration; pBest i (n) is the current individual optimal solution of the i-th particle; gBest(n) is the current global optimal solution of the particle swarm.

8. A signal interference suppression device, characterized in that, Include: A memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it executes the signal interference suppression method according to any one of claims 1-7.

9. A signal receiving device, characterized in that, Include: An array antenna; The array antenna includes: the signal interference suppression device according to claim 8 and N antenna elements; N≥2。 10. A communication system, characterized in that, Include: A target signal transmitting end and the signal receiving device according to claim 9; Wherein, the target signal transmitting end is used to transmit signals to the signal receiving device.

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