CN-122026962-A - Low-altitude holographic MIMO channel optimization fitting method assisted by laminated intelligent super surface

CN122026962ACN 122026962 ACN122026962 ACN 122026962ACN-122026962-A

Abstract

The invention relates to a laminated intelligent super-surface-assisted low-altitude holographic MIMO channel optimization fitting method, and belongs to the technical field of digital communication. The method comprises the steps of initializing system parameters, determining beamforming matrixes of a TX-SIM and an RX-SIM, constructing a spatial correlation matrix of a receiving end, determining scaling factors of a spatial correlation holographic MIMO channel matrix, a singular value matrix and an end-to-end channel, calculating channel fitting errors corresponding to preset groups of super-atomic phase shift configurations, iteratively optimizing the super-atomic phase shift by using a linear decreasing particle swarm optimization method with the minimized channel fitting errors as an optimization target, and obtaining and outputting optimal phase shift configurations. The invention effectively solves the optimization problem caused by multilayer coupling and non-convex constraint of the laminated intelligent super surface in the low-altitude dynamic environment, enhances the global optimizing capability, reduces the channel fitting error and improves the channel capacity and the communication quality of the holographic MIMO system.

Inventors

ZHANG XIAOTIAN
ZHAO JIA
BAO JIANRONG
LIU CHAO
JIANG BIN
SUN MINHONG
XU XIAORONG
LI LIANMING
CHEN JUN
CHEN YING

Assignees

杭州电子科技大学
杭州智简物链科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260413

Claims (9)

1. The low-altitude holographic MIMO channel optimization fitting method assisted by the laminated intelligent super surface is characterized by comprising the following steps of: S1, initializing system parameters, wherein the system parameters comprise wavelength, transmission coefficients of super atoms of each layer of the transmitting end-laminated intelligent super surface TX-SIM and transmission coefficients of super atoms of each layer of the receiving end-laminated intelligent super surface RX-SIM; based on the system parameters, determining the same-layer super-atomic spacing and the beam forming matrix of the TX-SIM and the same-layer super-atomic spacing and the beam forming matrix of the RX-SIM; s2, constructing space correlation matrixes of the TX-SIM and the RX-SIM based on the same-layer super-atomic spacing and the beam forming matrix of the TX-SIM and the same-layer super-atomic spacing and the beam forming matrix of the RX-SIM respectively, and further determining a space correlation holographic MIMO channel matrix, a singular value matrix and a scaling factor of an end-to-end channel; S3, calculating channel fitting errors respectively corresponding to preset groups of super-atomic phase shift configurations based on a beam forming matrix of the TX-SIM and the RX-SIM and scaling factors of a space-related holographic MIMO channel matrix, a singular value matrix and an end-to-end channel; S4, taking the minimized channel fitting error as an optimization target, and adopting a linear decreasing particle swarm optimization method to iteratively optimize the super-atomic phase shift of the TX-SIM and the RX-SIM to obtain an optimal phase shift configuration which minimizes the channel fitting error, wherein the optimal phase shift configuration comprises an optimized TX-SIM phase shift matrix, an optimized RX-SIM phase shift matrix and the channel fitting error; S5, taking the finally obtained channel fitting error as the optimal channel fitting error, and taking the corresponding TX-SIM phase shift matrix and RX-SIM phase shift matrix as the optimal phase shift output of the TX-SIM and RX-SIM respectively.
2. The laminated intelligent super surface aided low-altitude holographic MIMO channel optimization fitting method of claim 1, wherein in step S1, the co-layer super atomic distance and beam forming matrix of TX-SIM and the co-layer super atomic distance and beam forming matrix of RX-SIM are determined, specifically comprising: s1.1, for a holographic MIMO far field communication system based on a laminated intelligent super surface SIM, determining the transmission coefficient of a super atom based on the phase shift of the super atom on each layer of super surfaces of TX-SIM and RX-SIM, and further determining the transmission coefficient matrix of each layer of super surfaces of TX-SIM and RX-SIM; S1.2, calculating the super-atom spacing on the same-layer super-surface of the TX-SIM, the super-atom spacing on the super-surface of the adjacent layer and the super-atom spacing between the transmitting antenna and the first layer super-surface, so as to determine the electromagnetic wave transmission coefficients between the super-surface of the adjacent layer and between the transmitting antenna array and the super-surface; S1.3, calculating the super-atom spacing on the same-layer super-surface of the RX-SIM, the super-atom spacing on the super-surface of the adjacent layer and the super-atom spacing between the receiving antenna and the super-surface of the first layer, so as to determine the electromagnetic wave transmission coefficients between the super-surface of the adjacent layer and between the receiving antenna array and the super-surface; s1.4, constructing a beam forming matrix of the TX-SIM according to hierarchical linkage based on electromagnetic wave transmission coefficients between a transmission coefficient matrix of each layer of super surface of the TX-SIM and the super surface of the adjacent layer and between a transmitting antenna array and the super surface; based on the transmission coefficient matrix of each layer of super surface of RX-SIM and the electromagnetic wave transmission coefficient between the adjacent layer of super surface and between the receiving antenna array and the super surface, constructing the beam forming matrix of RX-SIM according to the hierarchy.
3. The stacked intelligent super surface aided low-altitude holographic MIMO channel optimization fitting method of claim 2, wherein the beamforming matrix of the TX-SIM is expressed as: Wherein, the 、、 Layer L and layer L of TX-SIM respectively A transmission coefficient matrix of the layer and the layer 1 super surface; 、 between the L-1 th layer and the L-th layer super surface of the TX-SIM respectively Layer (a) Electromagnetic wave transmission coefficients between the laminar surfaces; The transmission coefficient of electromagnetic waves between the transmitting antenna array and the layer 1 super surface; The beamforming matrix of the RX-SIM is expressed as: Wherein, the 、、 Layer 1, respectively RX-SIM A transmission coefficient matrix of the super surfaces of the layer and the K layer; 、 Respectively RX-SIM (second) Layer (a) Electromagnetic wave transmission coefficients between the layer super surfaces, between the K-1 layer and the K layer super surface; is the electromagnetic wave transmission coefficient between the layer 1 super surface and the receiving antenna array.
4. The laminated intelligent super surface aided low-altitude holographic MIMO channel optimization fitting method of claim 1, wherein in step S2, a spatial correlation matrix of TX-SIM and RX-SIM is constructed, and scaling factors of a predicted channel, the spatial correlation holographic MIMO channel and a singular value matrix are determined, specifically comprising: S2.1, respectively calculating a space correlation matrix of the TX-SIM and a space correlation matrix of the RX-SIM based on the same-layer super-atomic distance of the TX-SIM and the same-layer super-atomic distance of the RX-SIM; S2.2, generating a space correlation holographic MIMO channel matrix based on the space correlation matrix of the TX-SIM and the RX-SIM Wherein Is a Rayleigh fading channel matrix which is independently and uniformly distributed; S2.3 space-dependent holographic MIMO channel matrix Singular value decomposition is carried out to obtain a singular value matrix, and an end-to-end channel matrix is calculated; s2.4, calculating and obtaining the scaling factor of the end-to-end channel based on the least square relation between the end-to-end channel matrix and the singular value matrix.
5. The stacked intelligent super surface aided low-altitude holographic MIMO channel optimization fitting method of claim 1, wherein in step S3, for each set of super atomic phase shift configurations, its corresponding channel fitting error is calculated according to the following equation : Wherein, the For the scaling factor of the end-to-end channel, For the beamforming matrix of the TX-SIM, For the beamforming matrix of the RX-SIM, For a spatial correlated holographic MIMO channel matrix, As a matrix of singular values, The Frobenius norm is represented, i.e. the fitting error of the desired channel fitting problem is characterized by the Frobenius norm.
6. The laminated intelligent super-surface assisted low-altitude holographic MIMO channel optimization fitting method according to claim 1, wherein in step S4, the super-atomic phase shifts of TX-SIM and RX-SIM are iteratively optimized by using a linearly decreasing particle swarm optimization method, and specifically comprising: s4.1 initializing a set of random particles, each particle i being assigned a position Sum speed of Two attributes, wherein the position Super atomic phase shift combination representing TX-SIM and RX-SIM, speed Representing the amplitude of the phase shift variation; S4.2, the channel fitting errors of the multiple groups of super-atomic phase shift configurations calculated in the step S3 are calculated As input parameters, calculate the fitness of each particle : ; S4.3, performing iterative optimization: Updating individual optimal solutions And a globally optimal solution ; Updating the velocity and position of the particles according to the following formula: Wherein, the Representing the number of current iterations and, 、 The speeds of the particles at the t and t+1 iterations are respectively shown, 、 Respectively representing the positions of the particles of the t and t+1 times of iteration, As the weight of the inertia is given, And Is a learning factor; s4.4, updating the inertia weight by adopting a linear decrementing strategy Learning factor And Then returning to the step S4.3 to continue iteration until reaching the iteration termination condition; and S4.5, outputting the optimized super-atomic phase shift combination and the channel fitting error, and obtaining an optimized TX-SIM phase shift matrix, an RX-SIM phase shift matrix and the channel fitting error.
7. The stacked intelligent super surface aided low-altitude holographic MIMO channel optimization fitting method of claim 6, wherein the inertial weights are updated using a linear decreasing strategy The expression is as follows: Wherein, the For the inertia weight of the t-th iteration, For the maximum number of iterations to be performed, And Respectively setting a maximum value and a minimum value; updating learning factors using a linearly decreasing strategy And The expression is as follows: Wherein, the 、 Learning factors for the t-th iteration respectively 、 , 、 Respectively are learning factors Is set to be the maximum value, the minimum value, 、 Respectively are learning factors Maximum and minimum values of (a) and (b).
8. A computer device comprising at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement the method of any of claims 1-7.
9. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of claims 1-7.

Description

Low-altitude holographic MIMO channel optimization fitting method assisted by laminated intelligent super surface Technical Field The invention belongs to the technical field of digital communication, and particularly relates to a laminated intelligent super-Surface (SIM) -assisted low-altitude holographic MIMO (Holographic Multiple Input Multiple Output, HMIMO) channel optimization fitting method. Background Innovative technologies of stacking smart supersurfaces (STACKED INTELLIGENT Metasurfaces, SIMs) have recently been demonstrated to enable advanced signal processing directly in the native Electromagnetic (EM) wave domain. Unlike a conventional MIMO system having only active antennas, a SIM is tightly integrated with a transmitting end (TX) and a receiving end (RX) to enhance quality of service (QoS). When the technology is applied to a low-altitude communication scene, an innovative solution is provided for coping with challenges such as high-speed movement of nodes such as unmanned aerial vehicle groups and aerial platforms, complex channel variation, and line-of-sight chain Louis blocking. However, the low-altitude dynamic environment and the structure of the SIM itself bring serious optimization problems that the SIM is formed by stacking multiple layers of super-surfaces in a complex way, the performance of the SIM exceeds that of a single layer of super-surface, but high-coupling multi-layer combined regulation variables are introduced, and meanwhile, the regulation of each super-atom must follow non-convex constant-mode constraint. Under the low-altitude background, the channel with rapid change is deeply coupled with multiple layers of adjustable parameters of the SIM, so that the overall optimization problem of the system presents strong non-convexity, the traditional algorithm is extremely easy to be trapped into local optimum, and the due beam agility shaping and channel intelligent remodeling capabilities of the SIM in a dynamic space domain are difficult to realize. Therefore, exploring a new approach that avoids the locally optimal, efficient solution of this complex non-convex problem becomes a key challenge to releasing the full potential of low-altitude SIMs. In artificial electromagnetic materials, a superatom is the most basic building block that constitutes a hypersurface. This cell is typically sub-wavelength in size and has specific electromagnetic resonance characteristics (e.g., electrical resonance, magnetic resonance, etc.). By designing the shape, size, material and surrounding environment of the superatom, its response to an incident electromagnetic wave, i.e. the phase, amplitude, polarization and frequency of the wave it reflects or transmits, can be precisely controlled. When parameters such as a beam covariance matrix, a SIM phase shift matrix and the like are optimized, a non-convexity problem which is difficult to solve inevitably occurs, so that a method capable of optimizing the SIM phase shift in a high-efficiency and global manner is needed, thereby remarkably reducing the fitting error of a channel and improving the system capacity. Disclosure of Invention The invention aims to overcome the defects of the prior art, and provides a low-altitude holographic MIMO channel optimization fitting method assisted by a laminated intelligent super surface, so as to solve the problems of strong non-convexity, easy sinking into local optimum, low calculation efficiency and the like in the prior art when the holographic MIMO channel joint optimization assisted by the laminated intelligent super surface is processed in a low-altitude dynamic environment, realize global efficient optimization, obviously reduce channel fitting errors and improve system capacity. In order to achieve the purpose, the invention adopts the following technical scheme that the low-altitude holographic MIMO channel optimization fitting method assisted by the laminated intelligent super surface comprises the following steps: S1, initializing system parameters, wherein the system parameters comprise wavelength, transmission coefficients of super atoms of each layer of the transmitting end-laminated intelligent super surface TX-SIM and transmission coefficients of super atoms of each layer of the receiving end-laminated intelligent super surface RX-SIM; based on the system parameters, determining the same-layer super-atomic spacing and the beam forming matrix of the TX-SIM and the same-layer super-atomic spacing and the beam forming matrix of the RX-SIM; s2, constructing space correlation matrixes of the TX-SIM and the RX-SIM based on the same-layer super-atomic spacing and the beam forming matrix of the TX-SIM and the same-layer super-atomic spacing and the beam forming matrix of the RX-SIM respectively, and further determining a space correlation holographic MIMO channel matrix, a singular value matrix and a scaling factor of an end-to-end channel; S3, calculating channel fitting errors respectively corre