Hybrid beam forming method and system based on millimeter wave MU-mMIMO system

Abstract

The invention provides a hybrid beam forming method and system based on a millimeter wave MU-mMIMO system, and relates to the technical field of wireless communication, and the method comprises the steps: enabling an antenna array to be connected to a polarization reconfigurable array in a hybrid manner, carrying out the layout optimization of the antenna array, and constructing a signal model; constructing a millimeter wave MU-mMIMO polarization model based on the signal characteristics of polarization on the scale of the scattering cluster; optimizing a millimeter wave MU-mMIMO polarization model by taking polarization distribution of an antenna array and various parameters of beam forming as constraint conditions and taking an optimal sum speed as a target; based on the optimized millimeter wave MU-mMIMO polarization model, constructing an initial hybrid beam forming model; according to the target hybrid beam forming method and device, the technical problem that in the prior art, a traditional hybrid beam forming method neglects the polarization characteristic of a channel, and consequently the performance of a communication system is reduced can be solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a hybrid beamforming method and system based on a millimeter wave MU-mMIMO system. Background Art

[0002] Millimeter wave communication has become one of the most promising communication technologies due to its rich spectrum resources and very high transmission rate. Millimeter wave has a short wavelength, so a huge antenna array can be integrated on a smaller transceiver. This large-scale multiple-input multiple-output (MIMO) system can bring great gain to compensate for the path loss during transmission. However, millimeter waves are easily absorbed by the air, resulting in very serious path loss, so beamforming technology is needed to improve transmission quality and system capacity.

[0003] Hybrid beamforming plays a vital role in the MU-mMIMO communication architecture of millimeter wave (mmWave). This technology can fully utilize the flexibility of digital processing while maintaining the high energy efficiency of analog processing, thereby achieving an optimal balance between performance and energy consumption. Although the current research on hybrid beamforming is relatively mature, it usually ignores the polarization characteristics of the channel, which has an important impact on the performance optimization of actual communication systems. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a hybrid beamforming method and system based on a millimeter wave MU-mMIMO system, aiming to solve the technical problem that the traditional hybrid beamforming method in the prior art ignores the polarization characteristics of the channel, resulting in a decrease in the performance of the communication system.

[0005] A first aspect of the present invention is to provide a hybrid beamforming method based on a millimeter wave MU-mMIMO system, the method comprising: Hybridly connecting the antenna array to a polarization reconfigurable array, setting a switch component and a parasitic element in the polarization reconfigurable array, optimizing the antenna array layout, and constructing a signal model using the optimized antenna array; Based on the signal model and the signal characteristics of polarization at the scale of scattering clusters, a millimeter wave MU-mMIMO polarization model is constructed; Based on the millimeter wave MU-mMIMO polarization model, the millimeter wave MU-mMIMO polarization model is optimized with the polarization distribution of the antenna array and various parameters of beamforming as constraints and with the optimum and speed as the goals; Based on the optimized millimeter-wave MU-mMIMO polarization model, an initial hybrid beamforming model is constructed; The initial hybrid beamforming model is iteratively optimized according to an optimal perturbation genetic algorithm to obtain a target hybrid beamforming model.

[0006] Compared with the prior art, the beneficial effects of the present invention are as follows: the hybrid beamforming method based on the millimeter wave MU-mMIMO system provided by the present invention can effectively improve the performance of the communication system. Specifically, on the basis of using a polarization reconfigurable array, the design concept of a hybrid structure and a dynamic subarray structure is combined to reduce the performance degradation caused by a fixed subarray, improve the link quality of each user, and suppress user interference, which has significant improvement benefits for multi-user scenarios; introduce the polarization of the scattering cluster to improve the directional selection of the channel, which can effectively improve the spatial selectivity and scattering selectivity; optimize various parameters to minimize interference in the hybrid beamforming, and then iteratively optimize the array polarization distribution to achieve the convergence and performance of the communication system, and improve the speed of the communication system, thereby solving the technical problem that the traditional hybrid beamforming method in the prior art ignores the polarization characteristics of the channel, resulting in a degradation of the performance of the communication system.

[0007] According to one aspect of the above technical solution, the antenna array is hybrid-connected to a polarization reconfigurable array, a switch component and a parasitic element are set in the polarization reconfigurable array, the antenna array layout is optimized, and the signal model is constructed using the optimized antenna array, which specifically includes: A single-port polarization reconfigurable antenna is used to form an mMIMO antenna array, and the antenna array is divided into a vertical RF array group and horizontal RF array group , vertical RF array group and horizontal RF array group Hybrid connection to polarization reconfigurable array V(H); By setting switch components and parasitic elements in the polarization reconfigurable array, the connection mode between the polarization reconfigurable array and the antenna array is dynamically adjusted to optimize the antenna array layout, and a signal model is constructed using the optimized antenna array.

[0008] According to one aspect of the above technical solution, the construction formula of the antenna array to construct the signal model is as follows: The transmitted signal vector D is expressed as: , in, is the baseband beamforming matrix, express The matrix space of rows and columns is is the original data symbol vector, is the analog beamforming matrix, is the number of antennas at the transmitting end, for and The sum of the number of RF chains, is a vertical RF array group, is the horizontal RF array group, , in, Indicates Users; =1,2,3,...,U; that is, there are U RF chains to support the transmission of U streams, For users Polarization distribution of a polarization reconfigurable array V(H), wherein the polarization reconfigurable array V(H) includes a polarization reconfigurable array of V polarization dimension and a polarization reconfigurable array of H polarization dimension; If the user The polarization distribution Y is the polarization dimension V, then the user The received signal is expressed as: , in, For users The received signal in the V polarization dimension, , For users The received combined vector, for The conjugate transpose of For users The number of antennas, For other users The original data symbol vector, For other users The corresponding baseband beamforming matrix, From the transmitter in the X polarization dimension to the user in the V polarization dimension The polarization channel, where , , For other users of V polarization dimension To V polarization dimension users The polarization channel, For other users of H polarization dimension To V polarization dimension users The polarization channel, is zero-mean and has a covariance of A circularly symmetric Gaussian distributed additive noise vector.

[0009] According to one aspect of the above technical solution, based on the signal model and the signal characteristics of polarization at the scale of the scattering cluster, the step of constructing the millimeter wave MU-mMIMO polarization model specifically includes: Based on the signal model, a scattering cluster of each user is obtained, each scattering cluster has several independent multipath components, and an initial millimeter wave MU-mMIMO polarization model is constructed. The formula is as follows: , in, For users The initial millimeter wave polarization channel, For users No. The first scattering cluster The complex gain of the independent multipath components, , For users No. The first scattering cluster The azimuth and elevation angles of the independent multipath components, , For users No. The first scattering cluster The azimuth and elevation angles of the multipath components leaving the and are the normalized receive and transmit array steering vectors, respectively, yes The conjugate transpose of ; =1,2,3,..., ; is the total number of scattering clusters; =1,2,3,..., ; is the total number of independent multipath components, For users The number of antennas; Based on the scattering clusters and independent multipath components, as well as the signal characteristics of polarization at the scale of the scattering clusters, a polarization distribution matrix is ​​constructed, and the formula is as follows: , in, For users No. The first scattering cluster The polarization distribution matrix of the independent multipath components, Indicates the components and users at the transmitting end The phase between the components, The component of the transmitting end in the V polarization dimension and the user in the V polarization dimension No. The first scattering cluster The phase between the independent multipath components, The H polarization dimension of the transmitter and the V polarization dimension of the user No. The first scattering cluster The phase between the independent multipath components, The components of the transmitter in the V polarization dimension and the user in the H polarization dimension No. The first scattering cluster The phase between the independent multipath components, is the component of the transmitter in the H polarization dimension and the user in the H polarization dimension No. The first scattering cluster The phase between the independent multipath components obeys The uniform distribution between and They represent the cross-polarization discrimination rate of the transmitting end in V polarization dimension and H polarization dimension, and the mean is And the standard deviation is The lognormal distribution of is an imaginary unit; Based on the initial millimeter wave MU-mMIMO polarization model and the polarization distribution matrix, a millimeter wave MU-mMIMO polarization model is constructed, and the formula is as follows: , in, Indicates the distance from the transmitter in the X-polarization dimension to the user in the Y-polarization dimension The millimeter wave polarization channel between , .

[0010] According to one aspect of the above technical solution, based on the millimeter wave MU-mMIMO polarization model, taking the polarization distribution of the antenna array and various parameters of beamforming as constraints, and taking the optimum and speed as the goal, the steps of optimizing the millimeter wave MU-mMIMO polarization model specifically include: Calculate the received signal to interference plus noise ratio of the user, and calculate the sum speed based on the received signal to interference plus noise ratio, the formula is as follows: , in, For users The received information to interference plus noise ratio, is the average total transmitted power, is the noise power, for and speed; Taking the polarization distribution of the antenna array and various beamforming parameters as constraints, and taking the optimum and speed as the goals, the millimeter wave MU-mMIMO polarization model is optimized, and the formula is as follows: , in, is the polarization distribution of the mask matrix, , are the number of antennas in V polarization dimension and H polarization dimension respectively, is the number of users in the V polarization dimension, is the serial number of the antenna, and They are the serial numbers of the V polarization dimension RF chain and the H polarization dimension RF chain, is the original data symbol vector The autocorrelation matrix of and are the constant modulus constraints of the V polarization dimension and H polarization dimension subarrays at the transmitting end, Simulate the constant modulus constraint of beamforming at the receiver, and Ensure that the total transmission power of the system is and ensure that power is evenly distributed among users.

[0011] According to one aspect of the above technical solution, the step of constructing an initial hybrid beamforming model based on the optimized millimeter wave MU-mMIMO polarization model specifically includes: The effective channel gain is maximized by conjugate transposing to achieve the diagonal entries of the hybrid beamforming, as follows: , Among them, H is the maximum effective channel; The baseband beamforming algorithm is used to interfere with the non-diagonal entries. The formula is as follows: , in, for The ZF beamforming matrix, for The MMSE beamforming matrix, Indicates the effective analog channel, , represents a block diagonal matrix By the diagonal composition, for The conjugate transpose of coefficient and The calculation formula is as follows: , Then add the mask matrix to the hybrid beamforming algorithm The selection effect is calculated as follows: , in, for No. Column No. elements, V polarization dimension , For the H polarization dimension , is the number of users in the V polarization dimension, is a vector Middle The phase of the element, , yes After vectorization elements.

[0012] According to one aspect of the above technical solution, the step of iteratively optimizing the initial hybrid beamforming model according to the optimal perturbation genetic algorithm to obtain the target hybrid beamforming model specifically includes: Let Q and E represent the population size and the number of iterations respectively, and each chromosome represents a mask matrix. The population calculation formula is as follows: , in, represents the population of the e-th iteration, yes Middle Chromosome No. mask matrix, assuming is the chromosome with the highest fitness in the e-th iteration, that is, the mask matrix with the highest fitness, that is, , In the vicinity of the chromosome with the highest fitness, any new chromosome can be generated by random mutation, as expressed below: , in, represents the gene number, express The negation of , the set is in the range [1, ] is composed of k numbers randomly generated from different numbers. After executing the formula Q times, the new population generated is as follows: , in, It is a population The chromosome with the highest fitness The result of nearby mutations, if chromosome Fitness Higher than chromosome Fitness , you need to replace the corresponding chromosome as follows: ; Based on the optimal perturbation genetic algorithm, an optimal mask matrix is ​​iteratively generated to obtain a target hybrid beam.

[0013] A second aspect of the present invention is to provide a hybrid beamforming system based on a millimeter wave MU-mMIMO system, wherein the hybrid beamforming system based on the millimeter wave MU-mMIMO system is used to implement the hybrid beamforming method based on the millimeter wave MU-mMIMO system, and the system includes: A signal model building module, used to hybridly connect the antenna array to a polarization reconfigurable array, set a switch component and a parasitic element in the polarization reconfigurable array, optimize the antenna array layout, and build a signal model using the optimized antenna array; A polarization model building module, used to build a millimeter wave MU-mMIMO polarization model based on the signal model and the signal characteristics of polarization at the scale of the scattering cluster; A polarization model optimization module, for optimizing the millimeter wave MU-mMIMO polarization model based on the millimeter wave MU-mMIMO polarization model, taking the polarization distribution of the antenna array and various parameters of beamforming as constraints, and taking the optimum and speed as the goals; Initial model construction, which is used to build an initial hybrid beamforming model based on the optimized mmWave MU-mMIMO polarization model; The target model is constructed to iteratively optimize the initial hybrid beamforming model according to the optimal perturbation genetic algorithm to obtain a target hybrid beamforming model.

[0014] A third aspect of the present invention is to provide a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned hybrid beamforming method based on the millimeter wave MU-mMIMO system.

[0015] The fourth aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the hybrid beamforming method based on the millimeter wave MU-mMIMO system are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 4 is a flow chart of a hybrid beamforming method based on a millimeter wave MU-mMIMO system in a first embodiment of the present invention; Figure 2 is a structural block diagram of a millimeter wave MU-mMIMO system in a first embodiment of the present invention; Figure 3 The sum speed and signal-to-noise ratio test diagrams of different hybrid beamforming models; Figure 4 Test diagram of the speed and number of iterations of the hybrid beamforming model for different optimization algorithms; Figure 5 Test diagrams of speed and signal-to-noise ratio for hybrid beamforming models with different optimization algorithms; Figure 6 is the polarization distribution diagram of the mask matrix. DETAILED DESCRIPTION

[0017] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings. Several embodiments of the present invention are shown in the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0018] Embodiment 1 See also Figure 1 , as shown in the first embodiment of the present invention, a hybrid beamforming method based on a millimeter wave MU-mMIMO system is provided, and the method includes steps S10 to S14: Step S10, hybrid connecting the antenna array to a polarization reconfigurable array, setting a switch component and a parasitic element in the polarization reconfigurable array, optimizing the antenna array layout, and constructing a signal model using the optimized antenna array; Specifically, a single-port polarization reconfigurable antenna is used to form an mMIMO antenna array, and the antenna array is divided into a vertical RF array group. and horizontal RF array group , vertical RF array group and horizontal RF array group Hybrid connection to polarization reconfigurable array V(H); By setting switch components and parasitic elements in the polarization reconfigurable array, the connection mode between the polarization reconfigurable array and the antenna array is dynamically adjusted to optimize the antenna array layout, and a signal model is constructed using the optimized antenna array.

[0019] like Figure 2 As shown in FIG. 1 , a single-port polarization reconfigurable antenna is used to form a transmitting mMIMO antenna array. This antenna avoids the increase in RF (baseband) processing, power consumption, and feeding complexity caused by dual-polarization antennas. The change of antenna polarization can be achieved using a polarization selection circuit, that is, by using switches and parasitic elements to adjust the polarization state of the single-port polarization reconfigurable antenna, the polarization reconfigurable array V(H) produces different sub-array structures, and the vertical RF array group and horizontal RF array group Maintaining hybrid connectivity, vertical RF array group and horizontal RF array group All RF chains in the proposed method are connected to all elements of the polarization reconfigurable array V(H). Based on the use of the polarization reconfigurable array, the design concepts of the hybrid structure and the dynamic subarray structure are combined to alleviate the performance degradation caused by the fixed subarray.

[0020] like Figure 2 As shown, it is assumed that on the RF side of the base station (BS), the number of antennas used is Polarization reconfigurable array (uniform planar array), and The sum of the number of RF chains is . The system has users, each with The single-polarized uniform linear array of array elements and the application of a combiner are used to obtain the receiving combined vector , for The conjugate transpose of , and the BS uses U RF chains to support The transmission of a stream, .

[0021] It should be noted that the BS first uses a digital beamformer to digitally modify the data stream at the baseband to obtain a baseband beamforming matrix, then up-converts the processed signal to the carrier frequency through the RF chain, and then processes it by an analog beamformer to obtain an analog beamforming matrix.

[0022] Therefore, the construction formula of the antenna array signal model is as follows: The transmitted signal vector D is expressed as: , in, is the baseband beamforming matrix, express The matrix space of rows and columns is is the original data symbol vector, is the analog beamforming matrix, is the number of antennas at the transmitting end, for and The sum of the number of RF chains, is a vertical RF array group, It is the horizontal RF array group.

[0023] It should be noted that we assume = , is the original data symbol vector The autocorrelation matrix reflects the power distribution of the transmitted signal, where P is the average total transmit power, considering that the power distribution of each stream is equal. It is an analog beamforming matrix of an analog beamformer implemented with phase shifters, so it has constant modulus constraints. In the traditional structure, is fixed and regular, and considering Figure 2 When the dynamic polariton array structure is The distribution of elements in is related to the polarization distribution of the array, so, The calculation formula is as follows, , in, Indicates Users; =1,2,3,...,U; that is, there are U RF chains to support the transmission of U streams, For users Polarization distribution of a polarization reconfigurable array V(H), wherein the polarization reconfigurable array V(H) includes a polarization reconfigurable array of V polarization dimension and a polarization reconfigurable array of H polarization dimension; It should be noted that for users , which receives the signal It can be roughly divided into three parts, the first of which is the user The second part is the useful signal required by other users. The interference caused can be divided into interference between co-polarization users and interference between users caused by polarization mismatch; the third part is noise interference.

[0024] Therefore, if the user The polarization distribution Y is the polarization dimension V, then the user The received signal is expressed as: , in, For users The received signal in the V polarization dimension, , For users The received combined vector, for The conjugate transpose of For users The number of antennas, For other users The original data symbol vector, For other users The corresponding baseband beamforming matrix, From the transmitter in the X polarization dimension to the user in the V polarization dimension The polarization channel, where , , For other users of V polarization dimension To V polarization dimension users The polarization channel, For other users of H polarization dimension To V polarization dimension users The polarization channel, is zero-mean and has a covariance of A circularly symmetric Gaussian distributed additive noise vector.

[0025] Step S11, constructing a millimeter wave MU-mMIMO polarization model based on the signal model and the signal characteristics of polarization at the scale of the scattering cluster; It should be noted that due to the high path loss and high spatial correlation characteristics of the millimeter wave channel, some directions in the millimeter wave channel are statistically more likely to contain signal components than other directions, which will lead to limited spatial selectivity or scattering. And due to the shorter wavelength of millimeter waves, this leads to the fact that we usually ignore the factor of scattering clusters.

[0026] Specifically, based on the signal model, a scattering cluster of each user is obtained, each scattering cluster has several independent multipath components, and an initial millimeter wave MU-mMIMO polarization model is constructed, and the formula is as follows: , in, For users The initial millimeter wave polarization channel, For users No. The first scattering cluster The complex gain of the independent multipath components, , For users No. The first scattering cluster The azimuth and elevation angles of the independent multipath components, , For users No. The first scattering cluster The azimuth and elevation angles of the multipath components leaving the and are the normalized receive and transmit array steering vectors, respectively, yes The conjugate transpose of ; =1,2,3,..., ; is the total number of scattering clusters; =1,2,3,..., ; is the total number of independent multipath components, For users The number of antennas; Based on the scattering clusters and independent multipath components, as well as the signal characteristics of polarization at the scale of the scattering clusters, a polarization distribution matrix is ​​constructed, and the formula is as follows: , in, For users No. The first scattering cluster The polarization distribution matrix of the independent multipath components, Indicates the components and users at the transmitting end The phase between the components, The component of the transmitting end in the V polarization dimension and the user in the V polarization dimension No. The first scattering cluster The phase between the independent multipath components, The H polarization dimension of the transmitter and the V polarization dimension of the user No. The first scattering cluster The phase between the independent multipath components, The components of the transmitter in the V polarization dimension and the user in the H polarization dimension No. The first scattering cluster The phase between the independent multipath components, is the component of the transmitter in the H polarization dimension and the user in the H polarization dimension No. The first scattering cluster The phase between the independent multipath components obeys The uniform distribution between and They represent the cross-polarization discrimination rate of the transmitting end in V polarization dimension and H polarization dimension, and the mean is And the standard deviation is The lognormal distribution of is an imaginary unit; Based on the initial millimeter wave MU-mMIMO polarization model and the polarization distribution matrix, a millimeter wave MU-mMIMO polarization model is constructed, and the formula is as follows: , in, Indicates the distance from the transmitter in the X-polarization dimension to the user in the Y-polarization dimension The millimeter wave polarization channel between , .

[0027] After introducing the polarization of the scattering cluster, the steering vector is no longer of size The vectors of the transmitting end arrays will be different even for the same cluster and the same independent multipath components when the polarization distribution is different.

[0028] Step S12, based on the millimeter wave MU-mMIMO polarization model, taking the polarization distribution of the antenna array and various parameters of beamforming as constraints, and taking the optimum and speed as the goals, optimizing the millimeter wave MU-mMIMO polarization model; Calculate the received signal to interference plus noise ratio of the user, and calculate the sum speed based on the received signal to interference plus noise ratio, the formula is as follows: , in, For users The received information to interference plus noise ratio, is the average total transmitted power, is the noise power, for and speed; Taking the polarization distribution of the antenna array and various beamforming parameters as constraints, and taking the optimum and speed as the goals, the millimeter wave MU-mMIMO polarization model is optimized, and the formula is as follows: , in, is the polarization distribution of the mask matrix, , are the number of antennas in V polarization dimension and H polarization dimension respectively, is the number of users in the V polarization dimension, is the serial number of the antenna, and They are the serial numbers of the V polarization dimension RF chain and the H polarization dimension RF chain, is the original data symbol vector The autocorrelation matrix of and are the constant modulus constraints of the V polarization dimension and H polarization dimension subarrays at the transmitting end, Simulate the constant modulus constraint of beamforming at the receiver, and Ensure that the total transmission power of the system is and ensure that power is evenly distributed among users.

[0029] Among them, Figure 6 As shown, a mask matrix B is used to represent the polarization distribution of the array, element 1 represents the H polarization dimension, and element 0 represents the V polarization dimension.

[0030] Step S13, constructing an initial hybrid beamforming model based on the optimized millimeter wave MU-mMIMO polarization model; The diagonal entries of the effective simulated channel represent the gain, while the non-diagonal entries represent the simulated multi-stream interference. Therefore, the diagonal entries of the hybrid beamforming can be conjugate transposed to maximize the effective channel gain. Then, the baseband beamforming algorithm of the low-dimensional baseband beamformer is used to manage the diagonal entry interference, and the Mask-SVD (MSVD) algorithm is proposed.

[0031] The effective channel gain is maximized by conjugate transposing to achieve the diagonal entries of the hybrid beamforming, as follows:

[0032] Among them, H is the maximum effective channel; The ZF (zero forcing) beamforming algorithm or the MMSE (minimum mean square error) beamforming algorithm in the baseband beamforming algorithm is used to interfere with the non-diagonal entries. The formula is as follows: , in, for The ZF beamforming matrix, for The MMSE beamforming matrix, Indicates the effective analog channel, , represents a block diagonal matrix By the diagonal composition, for The conjugate transpose of coefficient and The calculation formula is as follows: , Then add the mask matrix to the hybrid beamforming algorithm The selection effect is calculated as follows: , in, for No. Column No. elements, V polarization dimension , For the H polarization dimension , is the number of users in the V polarization dimension, is a vector Middle The phase of the element, , yes After vectorization elements.

[0033] It should be noted that by designing a complementary mask matrix, the two polarization reconfigurable arrays (V polarization dimension and H polarization dimension) are accurately divided in quantity and spatial position, thereby reflecting the specific form of the array polarization distribution.

[0034] like Figure 6 As shown, a mask matrix B is used to represent the polarization distribution of the array, element 1 represents the H polarization dimension, element 0 represents the V polarization dimension, the V polarization dimension is the vertical polarization dimension, and the H polarization dimension is the horizontal polarization dimension. The V polarization dimension and the H polarization dimension are divided by the mask matrix and the inversion of the mask matrix.

[0035] Step S14, iteratively optimizing the initial hybrid beamforming model according to the optimal perturbation genetic algorithm to obtain a target hybrid beamforming model.

[0036] Specifically, Q and E represent the population size and the number of iterations respectively, and each chromosome represents a mask matrix. The population calculation formula is as follows: , in, represents the population of the e-th iteration, yes Middle Chromosome No. mask matrix, assuming is the chromosome with the highest fitness in the e-th iteration, that is, the mask matrix with the highest fitness, that is, , In the vicinity of the chromosome with the highest fitness, any new chromosome can be generated by random mutation, that is, in the vicinity of the mask matrix with the highest fitness, any new mask matrix can be generated by random mutation, the expression is as follows: , in, represents the gene number, that is, the distribution number in the mask matrix, express The negation of , the set is in the range [1, ] is composed of k numbers randomly generated from different numbers. After executing the formula Q times, the new population generated is as follows: , in, It is a population The chromosome with the highest fitness The result of nearby mutations, if chromosome Fitness Higher than chromosome Fitness , then the corresponding chromosome needs to be replaced, that is, the fitness of the new mask matrix is ​​higher than the mask matrix with the highest fitness, then the mask matrix with the highest fitness needs to be replaced, as shown below: ; Based on the optimal perturbation genetic algorithm, an optimal mask matrix is ​​iteratively generated to obtain a target hybrid beam.

[0037] It should be noted that the optimal perturbation genetic algorithm (OP-GA) is used for iterative optimization to make the model diverse and prevent convergence to the local optimum. When the termination condition is met, the mask matrix with the best fitness value is the solution to the optimization problem.

[0038] Simulation experiment test Assume that the number of independent multipath components (MPCs) is 6 and the antenna spacing is The azimuth and elevation angles of arrival and departure of the array follow the Laplace distribution, with an average angle of Evenly distributed within the base station, with an angle extension of 10°. Number of antennas at the base station , the number of users is and the number of antennas at the user For the polarization data of the scattering cluster, we used an indoor indirect environment, which means as well as To simplify the discussion, it is assumed that the number of users polarized in the V polarization dimension and the number of users polarized in the H polarization dimension are the same, and the number of receiving antennas for each user is the same, but the polarization distribution and the number of antennas of the users can be different. When designing a hybrid beamforming algorithm, fixed polarization refers to an array polarization distribution in which H polarization array elements and V polarization array elements appear alternately.

[0039] like Figure 3 As shown in FIG. 1 , the performance of different hybrid beamforming models under a fixed mask matrix is ​​tested. In the MSVD (singular value decomposition algorithm), the MMSE (minimum mean square error) beamformer has a more significant performance gain than the ZF (zero forcing) beamformer. The F in the model of this embodiment RF The elements are randomly distributed, and the MSVD algorithm can take the distribution state of polarization into account in the hybrid beamforming design, ultimately bringing excellent spectral efficiency.

[0040] like Figure 4 As shown in Figure 1, the performance of the hybrid beamforming model with different optimization algorithms in terms of speed and number of iterations is compared. The simulated population size is 50, the crossover probability Pc is 0.8, and the mutation probability P m is 0.1, respectively, genetic algorithm (GA), optimal perturbation genetic algorithm (OP-GA), random combination algorithm (RC), fixed polarization system, where the fixed polarization system is Figure 6 As shown, the H polarization dimension and the V polarization dimension are staggered. The results show that the performance of all polarization optimization algorithms is better than that of fixed polarization systems, significantly improving the system and rate. This performance improvement is mainly attributed to the fact that the optimization algorithm dynamically adjusts the polarization distribution of the transmitting array during the iteration process, thereby making full use of the polarization mismatch characteristics to reduce multi-user interference under cross-polarization conditions. In addition, polarization plays an important role in suppressing sidelobe interference. This suppression effect is reflected in two aspects: first, the sidelobes of the polarization beams in the H(V) polarization dimension in the multi-polarization array will not interfere with the users in the V(H) polarization dimension; second, the undesired grating lobes of the beams in the H(V) polarization dimension are optimized by adjusting the distribution of polarization array elements, thereby reducing the interference of the beam sidelobes in the H(V) polarization dimension to the users in the H(V) polarization dimension. In summary, using the optimal perturbation genetic algorithm (OP-GA) to optimize the mask matrix to adjust the overall polarization distribution of the array will effectively improve the performance of the hybrid beamforming model.

[0041] like Figure 5 As shown, the performance of the speed and signal-to-noise ratio of the hybrid beamforming models of different optimization algorithms are compared and tested. As the signal-to-noise ratio changes, the hybrid beamforming method based on the millimeter wave MU-mMIMO system of this embodiment performs excellently.

[0042] In summary, this embodiment implements the optimization of the hybrid beamforming method based on the millimeter wave MU-mMIMO system, and the simulation experiment proves the feasibility and good convergence performance of the method of this embodiment.

[0043] Embodiment 2 A second embodiment of the present invention provides a hybrid beamforming system based on a millimeter wave MU-mMIMO system, the system comprising: A signal model building module, used to hybridly connect the antenna array to a polarization reconfigurable array, set a switch component and a parasitic element in the polarization reconfigurable array, optimize the antenna array layout, and build a signal model using the optimized antenna array; A polarization model building module, used to build a millimeter wave MU-mMIMO polarization model based on the signal model and the signal characteristics of polarization at the scale of the scattering cluster; A polarization model optimization module, for optimizing the millimeter wave MU-mMIMO polarization model based on the millimeter wave MU-mMIMO polarization model, taking the polarization distribution of the antenna array and various parameters of beamforming as constraints, and taking the optimum and speed as the goals; Initial model construction, which is used to build an initial hybrid beamforming model based on the optimized mmWave MU-mMIMO polarization model; The target model is constructed to iteratively optimize the initial hybrid beamforming model according to the optimal perturbation genetic algorithm to obtain a target hybrid beamforming model.

[0044] Compared with the prior art, the hybrid beamforming system based on the millimeter wave MU-mMIMO system shown in this embodiment can effectively improve the performance of the communication system. Specifically, the signal model construction module is combined with the design concept of the hybrid structure and the dynamic subarray structure to reduce the performance degradation caused by the fixed subarray, improve the link quality of each user and suppress user interference, which has significant improvement benefits for multi-user scenarios; the polarization of the scattering cluster is introduced through the polarization model construction module to improve the directional selection of the channel, which can effectively improve the spatial selectivity and scattering selectivity; the polarization model optimization module and the initial model are used to optimize various parameters so that the hybrid beamforming minimizes interference, and then the target model is used to iteratively optimize the array polarization distribution, so as to achieve the convergence and performance of the communication system and improve the speed of the communication system, thereby solving the technical problem that the traditional hybrid beamforming method in the prior art ignores the polarization characteristics of the channel, resulting in the degradation of the performance of the communication system.

[0045] Embodiment 3 The third embodiment of the present invention provides a storage medium on which computer instructions are stored. When the instructions are executed by a processor, the steps of the method described in the first embodiment are implemented. Embodiment 4 A fourth embodiment of the present invention provides a device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first embodiment when executing the program.

[0046] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0047] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be specifically implemented in any computer-readable storage medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, a "computer-readable storage medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0048] More specific examples (a non-exhaustive list) of computer-readable storage media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable storage medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0049] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0050] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0051] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A hybrid beamforming method based on a millimeter wave MU-mMIMO system, characterized in that: The method comprises: The antenna array is hybrid-connected to a polarization reconfigurable array, a switch component and a parasitic element are arranged in the polarization reconfigurable array, the antenna array layout is optimized, and a signal model is constructed using the optimized antenna array; Based on the signal model and the signal characteristics of polarization at the scale of scattering clusters, a millimeter wave MU-mMIMO polarization model is constructed; Based on the millimeter wave MU-mMIMO polarization model, the millimeter wave MU-mMIMO polarization model is optimized with the polarization distribution of the antenna array and various parameters of beamforming as constraints and with the optimum and speed as the goals; Based on the optimized millimeter-wave MU-mMIMO polarization model, an initial hybrid beamforming model is constructed; The initial hybrid beamforming model is iteratively optimized according to an optimal perturbation genetic algorithm to obtain a target hybrid beamforming model.

2. The hybrid beamforming method based on millimeter wave MU-mMIMO system according to claim 1, characterized in that: The steps of hybrid-connecting the antenna array to the polarization reconfigurable array, setting a switch component and a parasitic element in the polarization reconfigurable array, optimizing the antenna array layout, and constructing a signal model using the optimized antenna array specifically include: A single-port polarization reconfigurable antenna is used to form an mMIMO antenna array, and the antenna array is divided into a vertical RF array group and horizontal RF array group , vertical RF array group and horizontal RF array group Hybrid connection to polarization reconfigurable array V(H); By setting switch components and parasitic elements in the polarization reconfigurable array, the connection mode between the polarization reconfigurable array and the antenna array is dynamically adjusted to optimize the antenna array layout, and a signal model is constructed using the optimized antenna array.

3. The hybrid beamforming method based on millimeter wave MU-mMIMO system according to claim 2, characterized in that: The construction formula of the antenna array signal model is as follows: The transmitted signal vector D is expressed as: , in, is the baseband beamforming matrix, express The matrix space of rows and columns is is the original data symbol vector, is the analog beamforming matrix, is the number of antennas at the transmitting end, for and The sum of the number of RF chains, is a vertical RF array group, is the horizontal RF array group, ， in, Indicates Users; =1,2,3,...,U; that is, there are U RF chains to support the transmission of U streams, For users Polarization distribution of a polarization reconfigurable array V(H), wherein the polarization reconfigurable array V(H) includes a polarization reconfigurable array of V polarization dimension and a polarization reconfigurable array of H polarization dimension; If the user The polarization distribution Y is the polarization dimension V, then the user The received signal is expressed as: ， in, For users The received signal in the V polarization dimension, , For users The received combined vector, for The conjugate transpose of For users The number of antennas, For other users The original data symbol vector, For other users The corresponding baseband beamforming matrix, From the transmitter in the X polarization dimension to the user in the V polarization dimension The polarization channel, where , , For other users of V polarization dimension To V polarization dimension users The polarization channel, For other users of H polarization dimension To V polarization dimension users The polarization channel, is zero-mean and has a covariance of A circularly symmetric Gaussian distributed additive noise vector.

4. The hybrid beamforming method based on the millimeter wave MU-mMIMO system according to claim 3, characterized in that: Based on the signal model and the signal characteristics of polarization at the scale of the scattering cluster, the steps of constructing the millimeter wave MU-mMIMO polarization model specifically include: Based on the signal model, a scattering cluster of each user is obtained, each scattering cluster has several independent multipath components, and an initial millimeter wave MU-mMIMO polarization model is constructed. The formula is as follows: ， in, For users The initial millimeter wave polarization channel, For users No. The first scattering cluster The complex gain of the independent multipath components, , For users No. The first scattering cluster The azimuth and elevation angles of the independent multipath components, , For users No. The first scattering cluster The azimuth and elevation angles of the multipath components leaving the and are the normalized receive and transmit array steering vectors, respectively, yes The conjugate transpose of ; =1,2,3,..., ; is the total number of scattering clusters; =1,2,3,..., ; is the total number of independent multipath components, For users The number of antennas; Based on the scattering clusters and independent multipath components, as well as the signal characteristics of polarization at the scale of the scattering clusters, a polarization distribution matrix is ​​constructed, and the formula is as follows: ， in, For users No. The first scattering cluster The polarization distribution matrix of the independent multipath components, Indicates the components and users at the transmitter The phase between the components, The component of the transmitting end in the V polarization dimension and the user in the V polarization dimension No. The first scattering cluster The phase between the independent multipath components, The H polarization dimension of the transmitter and the V polarization dimension of the user No. The first scattering cluster The phase between the independent multipath components, The components of the transmitter in the V polarization dimension and the user in the H polarization dimension No. The first scattering cluster The phase between the independent multipath components, is the component of the transmitter in the H polarization dimension and the user in the H polarization dimension No. The first scattering cluster The phase between the independent multipath components is subject to The uniform distribution between and They represent the cross-polarization discrimination rate of the transmitting end in V polarization dimension and H polarization dimension, and the mean is And the standard deviation is The log-normal distribution of is an imaginary unit; Based on the initial millimeter wave MU-mMIMO polarization model and the polarization distribution matrix, a millimeter wave MU-mMIMO polarization model is constructed, and the formula is as follows: ， in, Indicates the distance from the transmitter in the X-polarization dimension to the user in the Y-polarization dimension The millimeter wave polarization channel between , .

5. The hybrid beamforming method based on millimeter wave MU-mMIMO system according to claim 4, characterized in that: Based on the millimeter wave MU-mMIMO polarization model, taking the polarization distribution of the antenna array and various parameters of beamforming as constraints, and taking the optimum and speed as the goal, the steps of optimizing the millimeter wave MU-mMIMO polarization model specifically include: Calculate the received signal to interference plus noise ratio of the user, and calculate the sum speed based on the received signal to interference plus noise ratio, the formula is as follows: ， in, For users The received information to interference plus noise ratio, is the average total transmitted power, is the noise power, for and speed; Taking the polarization distribution of the antenna array and various beamforming parameters as constraints, and taking the optimum and speed as the goals, the millimeter wave MU-mMIMO polarization model is optimized, and the formula is as follows: ， in, is the polarization distribution of the mask matrix, , are the number of antennas in V polarization dimension and H polarization dimension respectively, is the number of users in the V polarization dimension, is the serial number of the antenna, and They are the serial numbers of the V polarization dimension RF chain and the H polarization dimension RF chain, is the original data symbol vector The autocorrelation matrix of and are the constant modulus constraints of the V polarization dimension and H polarization dimension subarrays at the transmitting end, Simulate the constant modulus constraint of beamforming at the receiver, and Ensure that the total transmission power of the system is and ensure that power is evenly distributed among users.

6. The hybrid beamforming method based on millimeter wave MU-mMIMO system according to claim 5, characterized in that: The steps of constructing an initial hybrid beamforming model based on the optimized millimeter wave MU-mMIMO polarization model include: The effective channel gain is maximized by conjugate transposing to achieve the diagonal entries of the hybrid beamforming, as follows: ， Among them, H is the maximum effective channel; The baseband beamforming algorithm is used to interfere with the non-diagonal entries. The formula is as follows: ， in, for The ZF beamforming matrix, for The MMSE beamforming matrix, Indicates the effective analog channel, , represents a block diagonal matrix By the diagonal composition, for The conjugate transpose of coefficient and The calculation formula is as follows: ， Then add the mask matrix to the hybrid beamforming algorithm The selection effect is calculated as follows: ， in, for No. Column No. elements, V polarization dimension , For the H polarization dimension , is the number of users in the V polarization dimension, is a vector Middle The phase of an element, , yes After vectorization elements.

7. The hybrid beamforming method based on millimeter wave MU-mMIMO system according to claim 6, characterized in that: The step of iteratively optimizing the initial hybrid beamforming model according to the optimal perturbation genetic algorithm to obtain the target hybrid beamforming model specifically includes: Let Q and E represent the population size and the number of iterations respectively, and each chromosome represents a mask matrix. The population calculation formula is as follows: ， in, represents the population of the e-th iteration, yes Middle Chromosome No. mask matrix, assuming is the chromosome with the highest fitness in the e-th iteration, that is, the mask matrix with the highest fitness, that is, ， In the vicinity of the chromosome with the highest fitness, any new chromosome can be generated by random mutation, as expressed below: ， in, represents the gene number, express The negation of , the set is in the range [1, ] is composed of k numbers randomly generated from different numbers. After executing the formula Q times, the new population generated is as follows: ， in, It is a population The chromosome with the highest fitness The result of nearby mutations, if chromosome Fitness Higher than chromosome Fitness , you need to replace the corresponding chromosome as follows: ； Based on the optimal perturbation genetic algorithm, an optimal mask matrix is ​​iteratively generated to obtain a target hybrid beam.

8. A hybrid beamforming system based on a millimeter wave MU-mMIMO system, characterized in that: The system is used to implement the hybrid beamforming method based on the millimeter wave MU-mMIMO system according to any one of claims 1 to 7, and the system includes: A signal model building module, used to hybridly connect the antenna array to a polarization reconfigurable array, set a switch component and a parasitic element in the polarization reconfigurable array, optimize the antenna array layout, and build a signal model using the optimized antenna array; A polarization model building module, used to build a millimeter wave MU-mMIMO polarization model based on the signal model and the signal characteristics of polarization at the scale of the scattering cluster; A polarization model optimization module, for optimizing the millimeter wave MU-mMIMO polarization model based on the millimeter wave MU-mMIMO polarization model, taking the polarization distribution of the antenna array and various parameters of beamforming as constraints, and taking the optimum and speed as the goals; Initial model construction, which is used to build an initial hybrid beamforming model based on the optimized mmWave MU-mMIMO polarization model; The target model is constructed to iteratively optimize the initial hybrid beamforming model according to the optimal perturbation genetic algorithm to obtain a target hybrid beamforming model.

9. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the hybrid beamforming method based on the millimeter wave MU-mMIMO system are implemented as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the hybrid beamforming method based on the millimeter wave MU-mMIMO system as claimed in any one of claims 1 to 7 are implemented.

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