Hybrid precoding method and device for MIMO (Multiple Input Multiple Output) system based on Riemannian manifold optimization

Abstract

The invention relates to the technical field of antenna array hybrid precoding, and particularly discloses an MIMO system hybrid precoding method and device based on Riemannian manifold optimization, and the method comprises the steps: obtaining the channel state information of an MIMO system; determining an all-digital precoding matrix of the MIMO system according to the channel state information; determining a precoding optimization target of a hybrid precoder according to the full-digital precoding matrix of the MIMO system; performing optimization processing on the precoding optimization target according to a Riemannian adaptive quantum particle swarm algorithm until the Riemannian adaptive quantum particle swarm algorithm meets a termination condition, and obtaining a globally optimal analog precoding matrix; and according to the full-digital precoding matrix of the MIMO system and the globally optimal analog precoding matrix, constructing the hybrid precoder of the MIMO system. According to the MIMO system hybrid precoding method based on Riemannian manifold optimization provided by the invention, the high-dimensional, complex and popular calculation efficiency during hybrid precoding is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of antenna array hybrid precoding, and particularly relates to a MIMO system hybrid precoding method based on Riemann manifold optimization, a MIMO system hybrid precoding device based on Riemann manifold optimization, a storage medium and an electronic device. BACKGROUND

[0002] The working frequency band of a millimeter wave system is 30GHz-300GHz, and the characteristics of the frequency band can realize high-speed data transmission in a cellular network. The signal wavelength is short, and this physical characteristic enables the transceiver to integrate a large-scale antenna array; and the system can compensate for the path loss of the millimeter wave signal through the large-scale antenna array carrying a multiple-input multiple-output (MIMO) technology, so as to ensure that the receiving end obtains sufficient signal power.

[0003] However, the traditional MIMO system has a serious hardware bottleneck in the large-scale antenna scenario, and the precoding scheme thereof mainly includes two types. First, full analog beamforming is realized in the radio frequency (RF) stage, and only the signal phase can be adjusted by a phase shifter, and the signal amplitude cannot be controlled; second, full digital precoding is deployed in the baseband circuit stage, and the phase and amplitude of the input signal can be controlled at the same time. Both of the two schemes need to be equipped with an independent radio frequency chain for each antenna. In the large-scale MIMO system, this architecture will cause the hardware cost and power consumption to increase sharply, so that the traditional scheme is difficult to meet the engineering application requirements.

[0004] The hybrid precoding technology can solve the above problems, and is also the core scheme of the current millimeter wave MIMO system. It combines the advantages of the above two types of precoding, realizes high-dimensional analog radio frequency (RF) precoding through a phase shifter, and at the same time, is matched with low-dimensional digital baseband precoding; this architecture can greatly reduce the number of radio frequency chains - the number of radio frequency chains in the full digital scheme needs to be consistent with the number of antenna units, while in the hybrid precoding scheme, the number of radio frequency chains can be reduced to the number of data streams, which fundamentally reduces the hardware cost and power consumption, and adapts to the large-scale antenna scenario.

[0005] The core goal of hybrid precoding is to maximize the system spectral efficiency, which can be approximated as "minimizing the Euclidean distance between the full-digital optimal precoder and the hybrid precoder". Common methods to solve this problem include MO-AltMin, PE-AltMin and OMP, but they cannot fully utilize the intrinsic geometric properties of the problem and have certain defects. Riemannian popular optimization is a kind of optimization problem on a manifold with Riemannian geometric structure, which searches for the optimal solution on the manifold. Existing Riemannian adaptive swarm intelligence optimization algorithms are mainly aimed at specific manifolds with global parameterization and compactness, such as sphere, Stiefel manifold and Grassmann manifold. However, many practical problems involve manifolds that do not meet these properties, especially complex manifolds that are not compact or cannot be globally parameterized. Existing methods are difficult to effectively solve or show poor robustness and adaptability on such manifolds. In addition, existing Riemannian adaptive swarm intelligence optimization algorithms generally have high computational complexity. For example, the Riemannian PSO algorithm needs to perform inverse mapping of geodesic lines at each iteration, which has a large amount of calculation and is only applicable to specific manifolds. The improved CMA-ES algorithm relies on matrix decomposition when updating the covariance matrix, which cannot avoid the complex encoding process of the probability distribution. The improved DE algorithm needs to perform explicit orthogonalization in the tangent space, further increasing the computational burden.

[0006] Therefore, how to improve the computational efficiency of high-dimensional and complex manifolds during hybrid precoding to maximize the system spectral efficiency has become a technical problem to be solved by those skilled in the art. SUMMARY

[0007] The application provides a MIMO system hybrid precoding method based on Riemannian manifold optimization, a MIMO system hybrid precoding device based on Riemannian manifold optimization, a storage medium and an electronic device, which solve the problem that the high-dimensional and complex manifolds cannot be calculated efficiently during hybrid precoding in the MIMO system in the related art.

[0008] As a first aspect of the application, a MIMO system hybrid precoding method based on Riemannian manifold optimization is provided, which comprises:

[0009] Obtaining the channel state information of the MIMO system;

[0010] Determining the full-digital precoding matrix of the MIMO system according to the channel state information;

[0011] Determining the precoding optimization target of the hybrid precoder according to the full-digital precoding matrix of the MIMO system;

[0012] optimizing the precoding optimization target according to the Riemann-adaptive quantum particle swarm algorithm until the Riemann-adaptive quantum particle swarm algorithm meets a termination condition, to obtain a globally optimal analog precoding matrix;

[0013] constructing a MIMO system hybrid precoder according to the MIMO system all-digital precoding matrix and the globally optimal analog precoding matrix.

[0014] Further, the optimizing the precoding optimization target according to the Riemann-adaptive quantum particle swarm algorithm until the Riemann-adaptive quantum particle swarm algorithm meets a termination condition, to obtain a globally optimal analog precoding matrix, comprises:

[0015] randomly generating an analog precoding matrix according to the precoding optimization target;

[0016] taking the randomly generated analog precoding matrix as an initial search centroid of the Riemann-adaptive quantum particle swarm algorithm;

[0017] initializing a particle swarm according to a tangent space where the initial search centroid is located, wherein each particle position in the particle swarm represents a candidate analog precoding matrix;

[0018] iteratively updating the particle positions in the particle swarm until a globally optimal analog precoding matrix is obtained.

[0019] Further, the iteratively updating the particle positions in the particle swarm until a globally optimal analog precoding matrix is obtained, comprises:

[0020] updating the particle swarm for the tangent space where the initial search centroid is located;

[0021] updating the individual best position, the global best position and the average best position in the particle swarm;

[0022] judging whether a tangent space termination iteration condition is met;

[0023] if the tangent space termination iteration condition is met, withdrawing the average best position as a new search centroid;

[0024] updating the particle swarm to the tangent space where the new search centroid is located;

[0025] repeating the updating process until a particle swarm termination iteration condition is met, to obtain a globally optimal analog precoding matrix.

[0026] Further, the updating the individual best position, the global best position and the average best position in the particle swarm, comprises:

[0027] In the tangent space with the current search center as the origin, multiple particle position update iterations are performed, wherein the update expression of each particle is:

[0028] ,

[0029] ,

[0030] wherein, represents the individual best position, represents the global best position of the population, represents the position of the i-th particle, , , , represents a contraction-expansion factor, represents the average best position.

[0031] Further, if the tangent space termination iteration condition is met, the average best position is withdrawn as a new search center, including:

[0032] The average best position of the particle swarm is calculated, and the average best position is withdrawn to the complex flow manifold, and the withdrawn average best position is taken as a new search center, wherein the complex flow manifold is a set of unit modulus constraints, and each element of each analog precoding matrix needs to satisfy the unit modulus constraint.

[0033] Further, the particle swarm is updated to the tangent space where the new search center is located, including:

[0034] The probability density function of the particle swarm is migrated to the tangent space where the new search center is located through a vector transport operator.

[0035] Further, the precoding optimization target of the hybrid precoder is determined according to the MIMO system full-digital precoding matrix, including:

[0036] According to the MIMO system full-digital precoding matrix, a minimization objective function is constructed with the analog precoding matrix as the optimization variable, and the expression of the minimization objective function is:

[0037] ,

[0038] wherein, represents the MIMO system full-digital precoding matrix, represents the analog precoding matrix, represents the current digital precoding matrix, each element of the analog precoding matrix needs to satisfy the unit modulus constraint, and the set of unit modulus constraints of all elements forms a complex flow row.

[0039] As another aspect of the present application, there is provided a Riemannian manifold optimization-based MIMO system hybrid precoding device for implementing the aforementioned Riemannian manifold optimization-based MIMO system hybrid precoding method, comprising:

[0040] An acquisition module is configured to acquire channel state information of the MIMO system.

[0041] A first determination module is configured to determine a MIMO system full-digital precoding matrix according to the channel state information.

[0042] A second determination module is configured to determine a precoding optimization target of the hybrid precoder according to the MIMO system full-digital precoding matrix.

[0043] An optimization processing module is configured to perform optimization processing on the precoding optimization target according to a Riemannian adaptive quantum particle swarm algorithm until the Riemannian adaptive quantum particle swarm algorithm meets a termination condition, and obtain a globally optimal analog precoding matrix.

[0044] A construction module is configured to construct a MIMO system hybrid precoder according to the MIMO system full-digital precoding matrix and the globally optimal analog precoding matrix.

[0045] As another aspect of the present application, there is provided a storage medium for storing computer instructions, which, when loaded and executed by a processor, implement the aforementioned Riemannian manifold optimization-based MIMO system hybrid precoding method.

[0046] As another aspect of the present application, there is provided an electronic device comprising a memory and a processor, wherein the processor is in communication connection with the memory, the memory is configured to store a computer program, and the processor is configured to load and execute the computer program to implement the aforementioned Riemannian manifold optimization-based MIMO system hybrid precoding method.

[0047] The Riemannian manifold optimization-based MIMO system hybrid precoding method provided by the present application ensures global exploration ability in the optimization process by taking the global search strategy with searching for the center as the core through the Riemannian adaptive quantum particle swarm algorithm, and realizes the transmission of the probability distribution of different tangent spaces through the vector transmission mechanism, thereby maintaining the continuity and consistency of the probability density function in the optimization process. The method is particularly suitable for solving complex Riemannian manifold optimization problems and has great advantages in optimization precision and convergence speed compared with the prior art, and thus can realize the hybrid precoding of the MIMO system, thereby fundamentally and effectively reducing the hardware cost and power consumption of the millimeter wave MIMO system. BRIEF DESCRIPTION OF DRAWINGS

[0048] The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute a part of this specification, illustrate embodiments of the application, and together with the description serve to explain the principles of the application.

[0049] Figure 1 The flow chart of the MIMO system hybrid precoding method based on Riemannian manifold optimization provided by the application.

[0050] Figure 2 The flow chart of the analog precoding matrix with global optimization provided by the application.

[0051] Figure 3 The schematic diagram of the search of the centroid update and the probability density function transmission provided by the application.

[0052] Figure 4 The schematic diagram of the average value and the standard deviation of the results of the PLSRGGr problem running 20 times provided by the application.

[0053] Figure 5 The schematic diagram of the average value and the standard deviation of the results of the maximum cut problem running 20 times provided by the application.

[0054] Figure 6 The schematic diagram of the average value and the standard deviation of the results of the joint diagonalization problem running 20 times provided by the application.

[0055] Figure 7 The schematic diagram of the average value and the standard deviation of the results of the semidefinite programming problem running 20 times provided by the application.

[0056] Figure 8 The schematic diagram of the spectrum efficiency performance comparison in the millimeter wave MIMO system hybrid precoding provided by the application.

[0057] Figure 9 The structure block diagram of the MIMO system hybrid precoding device based on Riemannian manifold optimization provided by the application.

[0058] Figure 10 The structure block diagram of the electronic device provided by the application. DETAILED DESCRIPTION

[0059] It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict. The application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0060] In order to make the technical scheme of the present application better understood, the technical scheme in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should belong to the protection scope of the present application.

[0061] It should be noted that the terms "first", "second" and the like in the description and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "comprise" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0062] In the present embodiment, a MIMO system hybrid precoding method based on Riemannian manifold optimization is provided, Figure 1 is a flow chart of the MIMO system hybrid precoding method based on Riemannian manifold optimization provided according to the embodiments of the present application, as Figure 1 shown, comprising:

[0063] S100, acquiring channel state information of a MIMO system;

[0064] S200, determining a MIMO system full-digital precoding matrix according to the channel state information;

[0065] In the embodiments of the present application, the channel state information of the MIMO system is acquired, the channel matrix of each subcarrier is singular value decomposed, and the left and right singular vectors corresponding to the maximum singular value are extracted as the optimal directions of the transmitting end and the receiving end, respectively. Then, the transmitting end matrix is power normalized to make each data stream have the same average power. In the presence of noise, the weighting matrix of the receiving end can also be calculated based on the minimum mean square error criterion to improve the anti-noise performance of the system, and the ideal full-digital precoding matrix is calculated accordingly.

[0066] S300, determining a precoding optimization target of a hybrid precoder according to the MIMO system full-digital precoding matrix;

[0067] Specifically, based on the MIMO system full-digital precoding matrix obtained by the above calculation, the problem of hybrid precoding is constructed as a minimization problem with the analog precoding matrix as the optimization variable, and based on the minimization problem, the optimization target of the hybrid precoder can be formed as the analog precoding matrix that realizes the minimization on the complex skew flow manifold.

[0068] S400, the precoding optimization target is optimized according to the Riemann adaptive quantum particle swarm algorithm until the Riemann adaptive quantum particle swarm algorithm meets the termination condition, and a globally optimal analog precoding matrix is obtained.

[0069] In the embodiment of the application, the analog precoding matrix is optimized on the complex skew flow manifold according to the Riemann adaptive quantum particle swarm algorithm. First, initialization is performed, and an analog precoding matrix is randomly generated on the complex skew flow manifold as the initial search center of the Riemann adaptive quantum particle swarm algorithm, and the particle swarm is initialized based on the tangent space thereof, and each particle position represents a candidate analog precoder matrix.

[0070] S500, the MIMO system hybrid precoder is constructed according to the MIMO system full-digital precoding matrix and the globally optimal analog precoding matrix.

[0071] In the embodiment of the application, after the above iteration update process is repeated, when the Riemann adaptive quantum particle swarm algorithm meets the termination condition, the globally optimal analog precoding matrix is output, and the corresponding digital precoding matrix is calculated based on the globally optimal analog precoding matrix and through the least square method, so as to construct a complete hybrid precoder.

[0072] In summary, the MIMO system hybrid precoding method based on Riemann manifold optimization provided by the application, in the hybrid precoding implementation process of the MIMO system, through the global search strategy with the search center as the core of the Riemann adaptive quantum particle swarm algorithm, the global exploration ability in the optimization process is ensured, and through the vector transmission mechanism, the probability distribution transmission of different tangent spaces of the center is realized, the continuity and consistency of the probability density function in the optimization process are maintained, and the method is especially suitable for solving complex Riemann manifold optimization problems, and has great advantages in optimization precision and convergence speed compared with the prior art, so that the hybrid precoding of the MIMO system can be realized, thereby fundamentally and effectively reducing the hardware cost and power consumption of the millimeter wave MIMO system.

[0073] In the embodiment of the application, the precoding optimization target is optimized according to the Riemann adaptive quantum particle swarm algorithm until the Riemann adaptive quantum particle swarm algorithm meets the termination condition, and a globally optimal analog precoding matrix is obtained, as shown in Figure 2 , comprising:

[0074] S410, randomly generating a simulation precoding matrix according to the precoding optimization target;

[0075] S420, taking the randomly generated simulation precoding matrix as an initial search center of the Riemann adaptive quantum particle swarm algorithm;

[0076] Specifically, an initial search center is randomly generated on a Riemannian manifold Based on the tangent space where the initial search center is located, a particle swarm is initialized.

[0077] S430, initializing a particle swarm according to the tangent space where the initial search center is located, wherein each particle position in the particle swarm represents a candidate simulation precoding matrix;

[0078] S440, iteratively updating the particle positions in the particle swarm until a globally optimal simulation precoding matrix is obtained.

[0079] Specifically, iteratively updating the particle positions in the particle swarm until a globally optimal simulation precoding matrix is obtained, including:

[0080] 1) updating the particle swarm for the tangent space where the initial search center is located;

[0081] 2) updating the individual best position, the global best position and the average best position in the particle swarm;

[0082] Specifically, updating the individual best position, the global best position and the average best position in the particle swarm, including:

[0083] In the tangent space with the current search center as the origin, multiple particle position update iterations are performed, wherein the update expression of each particle is:

[0084] ,

[0085] ,

[0086] wherein, represents the individual best position, represents the global best position of the population, represents the i-th particle position, , , , represents a contraction-expansion factor, represents the average best position.

[0087] 3) determining whether a tangent space termination iteration condition is met; ​​

[0088] In the embodiments of the present application, the iteration number of the Riemannian adaptive quantum-behaved particle swarm optimization algorithm in the tangent space can be determined, and if the iteration number reaches a preset number, the tangent space termination iteration condition is met, otherwise the termination iteration condition is not met.

[0089] 4) If the tangent space termination iteration condition is met, the average best position is withdrawn as a new search center;

[0090] Specifically, if the tangent space termination iteration condition is met, the average best position is withdrawn as a new search center, including:

[0091] The average best position of the particle swarm is calculated, and the average best position is withdrawn to a complex skew manifold, and the withdrawn average best position is used as a new search center, wherein the complex skew manifold is a set of unit modulus constraints, and each element of each simulated precoding matrix needs to meet the unit modulus constraint.

[0092] In the embodiments of the present application, the average best position of the particle swarm is calculated , and is withdrawn to a manifold as a new search center :

[0093]

[0094]

[0095] wherein, represents a withdrawal operator, which maps a point in the tangent space back to the manifold.

[0096] 5) The particle swarm is updated to the tangent space where the new search center is located;

[0097] Specifically, the particle swarm is updated to the tangent space where the new search center is located, including:

[0098] The probability density function of the particle swarm is migrated to the tangent space where the new search center is located through a vector transport operator.

[0099] In the embodiments of the present application, the probability density function of the particle swarm is migrated to the tangent space where the new center is located :

[0100]

[0101] wherein, is a vector transport operator, which specifically transports a tangent vector from one point to another point, as shown in Figure 3 .

[0102] ​6) repeat the above update process until the particle swarm termination iteration condition is met, and obtain the globally optimal simulation precoding matrix.

[0103] It should be understood that the search centroid mechanism is introduced, in each iteration process, the search centroid is dynamically selected and adjusted based on the distribution characteristics of the current particle swarm, the global exploration ability of the algorithm is enhanced, the above steps are repeated until the termination condition is met, and the global optimal solution is output.

[0104] Therefore, in the embodiment of the application, when the particle swarm is iteratively updated, the global search strategy with the search centroid as the core ensures the global exploration ability in the optimization process, and the probability distribution transmission mechanism of different tangent spaces of the centroid is realized, the continuity and consistency of the probability density function in the optimization process are maintained, the original constrained problem is converted into a series of unconstrained problems in the tangent space by using the geometric structure of the manifold, the universality and calculation efficiency of the algorithm are improved, the average best position of the tangent space is mapped to the manifold by the backtracking operator, the local optimization result can be correctly transmitted to the global search process, the algorithm can process local geometric structure and maintain the effectiveness of global optimization, the search direction and range can be dynamically adjusted by introducing the search centroid mechanism, and the performance and stability of the algorithm in processing complex non-convex optimization problems are improved.

[0105] In the embodiment of the application, the precoding optimization target of the hybrid precoder is determined according to the MIMO system full-digital precoding matrix, including:

[0106] According to the MIMO system full-digital precoding matrix, a minimization objective function with the simulation precoding matrix as the optimization variable is constructed, and the expression of the minimization objective function is:

[0107] ,

[0108] Wherein, The MIMO system full-digital precoding matrix is represented by W, The simulation precoding matrix is represented by W, The current digital precoding matrix is represented by W, each element in the simulation precoding matrix needs to satisfy the unit modulus constraint, and the set of unit modulus constraints of all elements forms a complex hyperplane.

[0109] It should be understood that the MIMO system hybrid precoding method based on Riemannian manifold optimization models the search space of the simulation precoding matrix as a specific Riemannian manifold, and adopts the Riemannian adaptive quantum-behaved particle swarm optimization algorithm to search on the manifold to find the globally optimal or approximately globally optimal simulation precoding matrix. The design problem of hybrid precoder is constructed as an optimization problem of minimizing an objective function with respect to the optimization variable

[0110]

[0111] where is the digital precoding matrix. Each element of the analog precoding matrix satisfies the unit modulus constraint, which naturally forms a complex oblique manifold. In addition, on the complex oblique manifold, the analog precoding matrix is optimized by using the Riemannian adaptive quantum-behaved particle swarm optimization algorithm. First, an analog precoding matrix is randomly generated on the complex oblique manifold as the initial search centroid of the Riemannian adaptive quantum-behaved particle swarm optimization algorithm, and the particle swarm is initialized based on the tangent space thereof. Each particle position represents a candidate analog precoding matrix . The above process is repeated until convergence. When the Riemannian adaptive quantum-behaved particle swarm optimization algorithm meets the termination condition, the globally optimal analog precoding matrix is output. Based on , the corresponding digital precoding matrix is calculated by using the least square method, and the complete hybrid precoder is constructed.

[0112] To verify the effectiveness of the embodiments of the present application, first, experiments are performed on four typical Riemannian manifold optimization problems, and the existing algorithms are compared:

[0113] (1) PLSRGGr model problem.

[0114] PLSRGGr (SIMPLSR with the generalized Grassmann Manifolds) is a SIMPLSR model based on the generalized Grassmann manifold. On this problem, the method of the present application obtains the best result in 20 out of 25 use cases, indicating that on relatively simple problems, the performance of the present method is not weaker than that of the algorithm with theoretical support. As shown in Figure 4 , the average value and standard deviation of the results of running 20 times of the PLSRGGr problem (the optimal result is dark, and the suboptimal result is light).

[0115] (2) Maximum cut problem.

[0116] The maximum cut problem (Maximum Cut, Maxcut) is a classic problem in graph theory, which can be represented in quadratic form on the oblique manifold. On this problem, the method of the present application obtains the optimal result on most of the test cases, indicating that the present method has an advantage in dealing with discrete non-convex optimization problems. As​Figure 5 Figure 20 shows the average and standard deviation of the results of the maximum cut problem (the optimal results are dark, and the suboptimal results are light) running 20 times.

[0117] (3) Joint Diagonalization problem.

[0118] The joint diagonalization problem (JD) can be expressed as an optimization problem on the Stiefel manifold. The joint diagonalization problem is defined on the Stiefel manifold, and since the geodesic is not reversible in this manifold, RQPSO, PSO, and DE are not tested in this group of experiments. On this problem, the method of the present application has a significant advantage over other algorithms on almost all test instances, indicating that the method of the present application performs excellently in dealing with optimization problems with complex curvature. As shown in Figure 6 Figure 21 shows the average and standard deviation of the results of the joint diagonalization problem (the optimal results are dark, and the suboptimal results are light) running 20 times.

[0119] (4) Semi-definite programming problem.

[0120] The elliptope semi-definite programming (SDP) problem is an optimization problem that combines semi-definite constraints with Oblique manifold geometric constraints. On this problem, the method of the present application has a significant advantage in 14 of the 25 test cases, indicating that the method of the present application is more adaptable to small-scale problems, and can still find high-quality solutions in the case of fewer variables. As shown in Figure 7 Figure 22 shows the average and standard deviation of the results of the semi-definite programming problem (the optimal results are dark, and the suboptimal results are light) running 20 times.

[0121] The above experiments are all based on benchmark problems, and the present application is applied to a millimeter wave MIMO system to carry out a series of experiments. On this problem, the present application has a significant advantage, as shown in Figure 8 Figure 23 shows a comparison of the spectral efficiency performance in the hybrid precoding of the millimeter wave MIMO system, wherein PAQPSO represents the Riemann adaptive quantum particle swarm optimization algorithm of the present application.

[0122] In conclusion, the MIMO system hybrid precoding method based on Riemann manifold optimization provided by the application, in the implementation process of the hybrid precoding of the MIMO system, the global search strategy with the search of the center as the core is ensured by the Riemann adaptive quantum particle swarm algorithm, the global exploration ability in the optimization process is ensured, and the probability distribution transmission of different tangent spaces is realized by the vector transmission mechanism, the continuity and consistency of the probability density function in the optimization process are maintained, and the method is especially suitable for solving complex Riemann manifold optimization problems, and has great advantages in optimization precision and convergence speed compared with the prior art, so that the hybrid precoding of the MIMO system can be realized, and the hardware cost and power consumption of the millimeter wave MIMO system can be effectively reduced fundamentally.

[0123] As another aspect of the application, a MIMO system hybrid precoding device 100 based on Riemann manifold optimization is provided for implementing the MIMO system hybrid precoding method based on Riemann manifold optimization described above, wherein, as shown in the figure, it comprises: Figure 9

[0124] The acquisition module 110 is configured to acquire the channel state information of the MIMI system.

[0125] The first determination module 120 is configured to determine the MIMO system full-digital precoding matrix according to the channel state information.

[0126] The second determination module 130 is configured to determine the precoding optimization target of the hybrid precoder according to the MIMO system full-digital precoding matrix.

[0127] The optimization processing module 140 is configured to perform optimization processing on the precoding optimization target according to the Riemann adaptive quantum particle swarm algorithm until the Riemann adaptive quantum particle swarm algorithm meets the termination condition, and obtain the globally optimal analog precoding matrix.

[0128] The construction module 150 is configured to construct the MIMO system hybrid precoder according to the MIMO system full-digital precoding matrix and the globally optimal analog precoding matrix.

[0129] ​The application provides a MIMO system hybrid precoding device based on Riemann manifold optimization.

[0130] The specific working principle of the MIMO system hybrid precoding device based on Riemann manifold optimization can be referred to the description of the MIMO system hybrid precoding method based on Riemann manifold optimization.

[0131] As another embodiment of the application, a computer storage medium is provided, which is used for storing a computer program, and the computer program is executed by a processor to implement the MIMO system hybrid precoding method based on Riemann manifold optimization.

[0132] In the embodiment of the application, a non-transitory computer readable storage medium is provided, which stores computer executable instructions, and the computer executable instructions are used to execute the MIMO system hybrid precoding method based on Riemann manifold optimization in any method embodiment.

[0133] As another embodiment of the application, an electronic device is provided, which comprises a memory and a processor, the processor is in communication connection with the memory, the memory is used for storing a computer program, and the processor is used for loading and executing the computer program to implement the MIMO system hybrid precoding method based on Riemann manifold optimization.

[0134] As Figure 10As shown, the electronic device 10 can include at least one processor 11, such as a CPU (Central Processing Unit), at least one communication interface 13, a memory 14, and at least one communication bus 12. The communication bus 12 is used to realize the connection and communication between the components. The communication interface 13 can include a display, a keyboard, and can also include a standard wired interface and a wireless interface. The memory 14 can be a high-speed RAM (Random Access Memory), or a non-volatile memory such as at least one disk memory. The memory 14 can also be at least one storage device located away from the aforementioned processor 11. The memory 14 stores an application program, and the processor 11 calls the program code stored in the memory 14 to execute any of the above method steps.

[0135] The communication bus 12 can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 12 can be divided into an address bus, a data bus, and a control bus. For ease of representation, Figure 10 Only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0136] The memory 14 can include a volatile memory such as a RAM (Random-Access Memory), and can also include a non-volatile memory such as a flash memory, a HDD (Hard Disk Drive), or a SSD (Solid-State Drive). The memory 14 can also include a combination of the above types of memories.

[0137] The processor 11 can be a CPU (Central Processing Unit), a network processor (NP), or a combination of a CPU and a NP.

[0138] The processor 11 can further include a hardware chip. The hardware chip can be an application-specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0139] Optionally, the memory 14 is further configured to store program instructions. The processor 11 can invoke the program instructions to implement the method of the present application Figure 1 The MIMO system hybrid precoding method based on Riemannian manifold optimization shown in the embodiments.

[0140] It can be understood that the above embodiments are only exemplary embodiments for illustrating the principles of the present application, and the present application is not limited thereto. Various modifications and improvements can be made by those of ordinary skill in the art without departing from the spirit and essence of the present application, and these modifications and improvements are also considered to be within the scope of protection of the present application.

Claims

1. A hybrid precoding method for MIMO systems based on Riemannian manifold optimization, characterized in that, include: Obtain channel state information of the MIMO system; Determine the all-digital precoding matrix of the MIMO system based on the channel state information; The precoding optimization objective of the hybrid precoder is determined based on the all-digital precoding matrix of the MIMO system. The precoding optimization objective is optimized according to the Riemann adaptive quantum particle swarm optimization algorithm until the Riemann adaptive quantum particle swarm optimization algorithm meets the termination condition, and the globally optimal simulated precoding matrix is ​​obtained. A hybrid precoder for the MIMO system is constructed based on the all-digital precoding matrix of the MIMO system and the globally optimal analog precoding matrix.

2. The hybrid precoding method for MIMO systems based on Riemannian manifold optimization according to claim 1, characterized in that, The precoding optimization objective is optimized using the Riemann adaptive quantum particle swarm optimization algorithm until the algorithm meets the termination condition, resulting in a globally optimal simulated precoding matrix, including: Randomly generate a simulated precoding matrix based on the precoding optimization objective; The randomly generated simulated precoding matrix is ​​used as the initial search centroid for the Riemann adaptive quantum particle swarm algorithm; The particle swarm is initialized based on the tangent space where the initial search centroid is located, wherein each particle position in the particle swarm represents a candidate analog precoding matrix; The positions of particles in the particle swarm are iteratively updated until the globally optimal simulation precoding matrix is ​​obtained.

3. The hybrid precoding method for MIMO systems based on Riemannian manifold optimization according to claim 2, characterized in that, The positions of particles in the particle swarm are iteratively updated until the globally optimal simulated precoding matrix is ​​obtained, including: Update the particle swarm for the tangent space containing the initial search centroid; The individual best position, global best position, and average best position in the particle swarm are all updated. Determine whether the tangent space termination iteration condition is met; If the tangent space termination iteration condition is met, then the best position of the average retreat is used as the new search centroid. Update the particle swarm to the tangent space where the new search centroid is located; Repeat the above update process until the particle swarm termination iteration condition is met, and obtain the globally optimal simulation precoding matrix.

4. The hybrid precoding method for MIMO systems based on Riemannian manifold optimization according to claim 3, characterized in that, The individual best position, global best position, and average best position in the particle swarm are all updated, including: Within the tangent space with the current search centroid as the origin, perform multiple particle position update iterations, where the update expression for each particle is: ， ， in, Indicates the best position for an individual. This represents the globally best position of the population. Indicates the first Particle positions, , , Indicates the contraction-expansion factor. This indicates the average best position.

5. The hybrid precoding method for MIMO systems based on Riemannian manifold optimization according to claim 3, characterized in that, If the tangent space termination condition is met, the best position of the pullback average is used as the new search centroid, including: The mean best position of the particle swarm is calculated, and the mean best position is reverted to the complex oblique manifold. The reverted mean best position is then used as the new search centroid, where the complex oblique manifold is a set of unity modulus constraints, and each element of each simulated precoding matrix must satisfy the unity modulus constraint.

6. The hybrid precoding method for MIMO systems based on Riemannian manifold optimization according to claim 3, characterized in that, Update the particle swarm to the tangent space of the new search centroid, including: The probability density function of the particle swarm is transferred to the tangent space of the new search centroid using a vector transfer operator.

7. The hybrid precoding method for MIMO systems based on Riemannian manifold optimization according to claim 1, characterized in that, The precoding optimization objectives of the hybrid precoder are determined based on the all-digital precoding matrix of the MIMO system, including: Based on the all-digital precoding matrix of the MIMO system, a minimization objective function is constructed with the analog precoding matrix as the optimization variable. The expression of the minimization objective function is as follows: ， in, This represents the all-digital precoding matrix of the MIMO system. Represents the analog precoding matrix. This represents the current digital precoding matrix. Each element in the analog precoding matrix must satisfy the unit modulus constraint, and the set of unit modulus constraints of all elements forms a complex skew stream.

8. A hybrid precoding apparatus for a MIMO system based on Riemannian manifold optimization, used to implement the hybrid precoding method for a MIMO system based on Riemannian manifold optimization as described in any one of claims 1 to 7, characterized in that, include: The acquisition module is used to acquire channel state information of the MIMO system; The first determining module is used to determine the all-digital precoding matrix of the MIMO system based on the channel state information; The second determining module is used to determine the precoding optimization target of the hybrid precoder based on the all-digital precoding matrix of the MIMO system; An optimization processing module is used to optimize the precoding optimization objective according to the Riemann adaptive quantum particle swarm optimization algorithm until the Riemann adaptive quantum particle swarm optimization algorithm meets the termination condition and obtains the globally optimal simulated precoding matrix. The module is used to construct a hybrid precoder for the MIMO system based on the all-digital precoding matrix of the MIMO system and the globally optimal analog precoding matrix.

9. A storage medium, characterized in that, Used to store computer instructions, which are loaded and executed by a processor to implement the hybrid precoding method for MIMO systems based on Riemannian manifold optimization as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, The system includes a memory and a processor, the processor being communicatively connected to the memory, the memory being used to store a computer program, and the processor being used to load and execute the computer program to implement the hybrid precoding method for MIMO systems based on Riemannian manifold optimization as described in any one of claims 1 to 7.

Images