Adaptive Beam Adjustment Model for Wireless Communication Overhead Reduction
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Solution Overview
Problem
Existing beamforming technologies face high system overhead and computational complexity, particularly in millimeter-wave communication systems with large antenna arrays, leading to increased time for beam searching and link establishment, which can be inadequate for real-time communication requirements.
Innovation Solution
An adaptive beamforming scheme is implemented using a trained beam adjustment model that optimizes beam adjustment overhead, leveraging neural networks to reduce system overhead while ensuring communication performance by maximizing the beam adjustment time interval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If non-codebook beamforming is used to achieve accurate channel state information, then communication performance is improved, but computational complexity and system overhead increase significantly
Solution Approach 1:
The patent pre-calculates and stores optimal beamforming weight vectors in a codebook during system initialization or offline training phase. These pre-computed beamforming parameters are based on predicted user equipment positions and movement directions, eliminating the need for real-time matrix inversion operations during actual beamforming execution.
Solution Approach 2:
The patent creates a simplified representation of complex channel state information by using predicted position and movement direction as input features. Instead of processing complete channel matrices in real-time, the system uses these copied essential parameters to select from pre-computed beamforming weight vectors, dramatically reducing computational load.
2Loss of information
If codebook-based beamforming is used to avoid channel state information acquisition, then system overhead is reduced, but beam searching time increases significantly with large antenna arrays
Solution Approach 1:
The patent pre-computes beamforming weight vectors for multiple possible user equipment positions and movement directions and stores them in a codebook before actual operation. During beamforming, the system only needs to query the codebook using predicted position and direction, avoiding exhaustive real-time beam searching while maintaining accurate beamforming performance.
Solution Approach 2:
The patent makes the codebook dynamic by incorporating user equipment movement direction predictions. Instead of using a static codebook that requires exhaustive searching, the system adapts the codebook selection based on predicted UE trajectory, reducing the search space to only relevant beam directions while maintaining comprehensive coverage.
3Reliability
If real-time beam adjustment is performed to maintain communication performance in dynamic scenarios, then communication reliability is improved, but processing overhead increases
Solution Approach 1:
The patent performs beamforming weight vector computation in advance based on predicted user equipment positions and movement directions. By pre-calculating these parameters offline or during low-traffic periods, the system eliminates the need for intensive real-time processing during actual beamforming operations, maintaining communication reliability while reducing instantaneous processing overhead.
Solution Approach 2:
The system uses predicted user equipment movement information to automatically select appropriate beamforming weight vectors from the pre-computed codebook without requiring continuous channel state feedback or real-time optimization calculations. This self-service approach maintains adaptive beamforming performance while minimizing processing overhead.
Data Source
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AI summary
An electronic apparatus and a method in a wireless communication system are disclosed. The electronic apparatus comprises processing circuitry configured to train a beam adjustment model for adaptive beamforming with training data, so that the beam adjustment model outputs, for any movement path of a user equipment, beam information which enables a beam adjustment time interval meeting a performance requirement of the user equipment to be optimized. According to embodiments of the disclosure, the beam adjustment model is trained by taking the beam adjustment time interval as an optimization target, and adaptive beamforming is performed using the trained beam adjustment model, so that it is possible to significantly reduce system overhead and measurement and processing overhead for beam adjustment, while ensuring communication performance of the user equipment.