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CN-122026961-A - Beam forming optimization system for 6G intelligent super surface

CN122026961ACN 122026961 ACN122026961 ACN 122026961ACN-122026961-A

Abstract

The invention relates to the technical field of wireless communication intelligent super-surfaces, and particularly discloses a beam forming optimization system for a 6G intelligent super-surface. The system comprises a channel sensing and predicting module, a digital twin simulation module, a distributed collaborative optimization module and a dynamic reconfiguration execution module, wherein the channel sensing and predicting module is used for collecting and predicting channel states in real time, the digital twin simulation module is used for performing high-fidelity performance simulation based on prediction, the distributed collaborative optimization module is used for parallelly solving optimal beamforming coefficients, and the dynamic reconfiguration execution module is used for safety configuration and has fault-tolerant switching capability. Through the cooperation of the modules, the invention can be suitable for channel variation in advance, finish optimization in millisecond level, ensure communication continuity under emergency, and further improve the reliability and stability of 6G communication.

Inventors

  • ZHOU CHAO
  • TAO SHA

Assignees

  • 铜陵学院

Dates

Publication Date
20260512
Application Date
20260304

Claims (10)

  1. 1. The beam forming optimization system for the 6G intelligent super surface is characterized by comprising the following components: The channel sensing and predicting module is used for acquiring and processing original channel state information from the intelligent super-surface array in real time and outputting multi-step predicting results of channel states in a future preset time window; the digital twin simulation module is used for constructing and running a virtual simulation model which is synchronous with the physical intelligent super surface and the wireless environment in high fidelity, simulating signal propagation processes of a plurality of moments in the future under different candidate beam forming strategies based on the multi-step prediction result output by the channel sensing and predicting module, and calculating a corresponding end-to-end performance index set; The distributed collaborative optimization module is used for parallelly solving an optimal beamforming coefficient matrix based on the end-to-end performance index set provided by the digital twin simulation module; And the dynamic reconfiguration execution module is used for configuring the optimal beamforming coefficient matrix calculated by the distributed collaborative optimization module to physical intelligent super-surface hardware.
  2. 2. The beamforming optimization system for 6G intelligent subsurface according to claim 1, wherein the channel sensing and prediction module comprises a space-time feature extraction unit and a long-term and short-term memory network prediction model; The space-time feature extraction unit processes original channel data of a plurality of continuous time slices by adopting a deep convolutional neural network so as to extract correlation features of channels in a space dimension and evolution trend features of the channels in the time dimension; The long-term and short-term memory network prediction model takes the space-time feature sequence extracted by the space-time feature extraction unit as input, and outputs the multi-step prediction result which comprises a predicted channel matrix and predicted channel quality indication parameters.
  3. 3. The 6G intelligent super surface oriented beamforming optimization system of claim 2, wherein the digital twin simulation module comprises an electromagnetic simulation model and a fast electromagnetic simulation engine; The electromagnetic simulation model comprises a geometric structure of an intelligent super surface, unit electromagnetic characteristics, a deployment environment three-dimensional map and typical scatterer distribution; The rapid electromagnetic simulation engine is based on a ray tracing and moment method mixed algorithm, simulates the multi-step prediction result in the electromagnetic simulation model as a driving input, and outputs the end-to-end performance index set which at least comprises a predicted signal-to-noise ratio, a predicted error rate and a predicted energy efficiency ratio.
  4. 4. The beam forming optimization system for the 6G intelligent super surface according to claim 3, wherein the distributed collaborative optimization module adopts a layered distributed architecture and comprises a central coordination node and a plurality of local optimization nodes distributed in subarray areas of the super surface; The central coordination node is used for receiving the global performance target and the constraint condition, decomposing the optimization task into a plurality of sub-problems and transmitting the sub-problems to each local optimization node; each local optimization node runs an improved particle swarm optimization algorithm, aims at maximizing local signal-to-noise ratio gain corresponding to a responsible subarray area, and simultaneously considers phase continuity constraint with an adjacent subarray to search an optimal local phase offset vector in a local parallel manner; the central coordination node is also used for periodically collecting the local optimal solutions output by all the local optimization nodes, running a global consistency fusion algorithm, coordinating the conflict among the local solutions by solving the convex optimization problem, and finally synthesizing a globally optimal beamforming coefficient matrix meeting the full array constraint.
  5. 5. The beamforming optimization system facing the 6G intelligent super surface according to claim 4, wherein the dynamic reconfiguration execution module comprises a configuration check unit and a fault tolerant switching unit; the configuration verification unit is used for firstly carrying out one-round quick verification simulation on the new configuration in the digital twin simulation module before sending a configuration instruction to the field programmable gate array of the super surface, ensuring that the performance is not lower than that of the current configuration, and sequentially writing configuration data packets into the control registers of the super surface units in a time division multiplexing mode through a control link after ensuring that the performance is not lower than that of the current configuration; The fault-tolerant switching unit is used for continuously monitoring the actual channel quality fed back by the channel sensing and predicting module, comparing the actual channel quality with the predicting performance of the digital twin simulation module, and when the actual channel quality is continuously lower than 90% of a predicted value in 3 continuous monitoring periods and the deviation exceeds a preset threshold value, judging that the current environment has unexpected rapid change and immediately triggering an emergency response flow; The emergency response flow comprises the steps of freezing iterative computation of the distributed collaborative optimization module, selecting the most suitable standby beam template from a plurality of pre-stored robust beam template libraries according to the rapid matching of the current channel characteristics, and instructing the dynamic reconfiguration execution module to immediately switch to the standby beam template.
  6. 6. The system of claim 5, wherein the modified particle swarm optimization algorithm defines a position vector for each particle that represents a candidate local phase offset combination, and a velocity vector that represents a direction and a step size of the phase adjustment; in each iteration, the particles update the speed and the position of the particles according to the individual historical optimal position and the group global optimal position; The improved particle swarm optimization algorithm introduces a self-adaptive inertia weight adjustment mechanism, and the self-adaptive inertia weight adjustment mechanism dynamically adjusts an inertia weight value according to the current iteration times and the convergence of the particle swarm; When the particle swarm diversity is too high, increasing the inertia weight to enhance the global exploration ability, and when the particle swarm tends to converge, reducing the inertia weight to enhance the local development accuracy; The improved particle swarm optimization algorithm constrains the position vector of each particle to have its value in each dimension within the discrete phase set achievable by the intelligent subsurface unit.
  7. 7. The 6G intelligent super surface oriented beamforming optimization system of claim 6, wherein the global consistency fusion algorithm regards the local phase offset vector output by each local optimization node as a solution to a sub-optimization problem; The central coordination node constructs a global objective function which is a weighted sum of local signal-to-noise ratio gains of all subarrays, and simultaneously introduces a group of coupling constraint items corresponding to Lagrangian multipliers for forcing the phase difference of units at the boundary of adjacent subarrays to approach 0; The central coordination node adopts an alternate direction multiplier method to solve the global optimization problem; in each iteration, the central coordination node firstly fixes Lagrangian multipliers and updates the local solutions of all the sub-problems in parallel; Then calculating phase inconsistency at the boundary according to the updated local solution, and updating the Lagrangian multiplier according to the phase inconsistency; the process is iterated until the phase difference at the boundaries of all adjacent subarrays is smaller than 0.1 radian, and all local solution combinations obtained at the moment form a globally consistent beamforming coefficient matrix.
  8. 8. The 6G intelligent super surface oriented beamforming optimization system of claim 7, wherein the robust beam template library is constructed as follows: In an off-line stage, the system carries out Monte Carlo simulation under a large number of typical severe channel scenes through the digital twin simulation module; For each scene, the system adopts a closed solution algorithm based on a maximum ratio transmission criterion to calculate a group of beamforming coefficients capable of still maintaining basic link connectivity under the worst condition; normalizing and quantifying the coefficients to form corresponding beam templates, labeling the corresponding channel characteristic labels, and storing the channel characteristic labels in a template library; And when the fault-tolerant switching unit performs online emergency matching, extracting core features of current instantaneous channel sensing data, performing quick cosine similarity calculation with labels of all templates in the beam template library, and selecting the template with the highest similarity as a standby beam.
  9. 9. The 6G intelligent super surface oriented beamforming optimization system of claim 8, wherein said system operates within a closed loop adaptive optimization framework; the closed-loop self-adaptive optimization framework takes 100 milliseconds as a basic optimization period, and the channel sensing and prediction module is started to complete channel data acquisition and prediction at the starting moment of each period; the digital twin simulation module and the distributed collaborative optimization module work in parallel, and simulation and optimization calculation are performed by using prediction information; Before the period is finished, the dynamic reconfiguration execution module completes verification and issuing of new beam configuration; the fault tolerant switching unit remains actively monitored throughout the cycle.
  10. 10. The beamforming optimization system for 6G intelligent super-surface according to claim 9, wherein the fast verification simulation performed by the configuration verification unit calculates an end-to-end performance index of the new beamforming coefficient matrix under the current prediction channel only, and determines that the configuration is valid if the new configuration has a prediction signal-to-noise ratio not lower than the configuration currently being used.

Description

Beam forming optimization system for 6G intelligent super surface Technical Field The invention belongs to the technical field of wireless communication and intelligent super-surface, and particularly relates to a beam forming optimization system for a 6G intelligent super-surface. Background In the technical field of wireless communication, the sixth generation mobile communication system aims at realizing global coverage, ultra-high reliability and extreme energy efficiency, and has the problems of dynamically adapting to complex and changeable propagation environments and efficiently utilizing scarce spectrum resources. The intelligent super surface is used as a novel electromagnetic material formed by a large number of programmable units, the phase and the amplitude of a wireless channel can be regulated and controlled in real time through software programming, and a revolutionary means is provided for constructing an intelligent controllable wireless environment. The beam forming technology is a key for realizing accurate signal coverage and energy focusing on the intelligent super surface. The beam forming technology intensively guides the energy of the transmitted signal to the target user by cooperatively adjusting the reflection coefficient of each unit of the super surface, thereby improving the intensity of the received signal and the communication quality. In the prior art, beamforming is generally designed based on static or quasi-static channel state information, and the optimization algorithm of the beamforming is difficult to deal with the rapid time-varying characteristic of a channel in a high-speed moving scene. When a user moves rapidly or encounters sudden occlusion, the existing system cannot sense and predict the severe change of a channel in real time, so that the pre-calculated optimal beam direction is rapidly invalid, and the frequent interruption of a communication link and serious signal quality fluctuation are caused. When the traditional optimization method is used for processing multi-objective balance of large-scale subsurface unit coordination and dynamic environment adaptability, the calculation complexity is high, the response delay is large, and the severe requirements of 6G application on ultra-low time delay and ultra-high reliability cannot be met. Therefore, a system that can adaptively optimize beamforming strategies in real-time to address dynamic environmental challenges and ensure robustness of the communication link is needed. Disclosure of Invention The invention aims to provide a beam forming optimization system for a 6G intelligent super surface, which aims to solve the problems that a beam forming scheme based on static channel state information in the prior art cannot cope with the rapid time-varying characteristics of a channel in a high-speed moving scene, so that a communication link is interrupted and the signal quality fluctuates, and the traditional optimization method has high calculation complexity and large response delay, and cannot meet the requirements of 6G ultra-low time delay and ultra-high reliability. The invention provides a beam forming optimization system facing a 6G intelligent super surface, which comprises the following steps: The channel sensing and predicting module is used for acquiring and processing original channel state information from the intelligent super-surface array in real time and outputting multi-step predicting results of channel states in a future preset time window; the digital twin simulation module is used for constructing and running a virtual simulation model which is synchronous with the physical intelligent super surface and the wireless environment in high fidelity, simulating signal propagation processes of a plurality of moments in the future under different candidate beam forming strategies based on the multi-step prediction result output by the channel sensing and predicting module, and calculating a corresponding end-to-end performance index set; The distributed collaborative optimization module is used for parallelly solving an optimal beamforming coefficient matrix based on the end-to-end performance index set provided by the digital twin simulation module; And the dynamic reconfiguration execution module is used for configuring the optimal beamforming coefficient matrix calculated by the distributed collaborative optimization module to physical intelligent super-surface hardware. Preferably, the channel sensing and predicting module comprises a space-time feature extraction unit and a long-term and short-term memory network predicting model; The space-time feature extraction unit processes original channel data of a plurality of continuous time slices by adopting a deep convolutional neural network so as to extract correlation features of channels in a space dimension and evolution trend features of the channels in the time dimension; The long-term and short-term memory network prediction model takes