Online Adaptive Real-Time Beamforming Design for Dynamic Environments in Cell-Free Systems

TL;DR

This paper introduces a high-generalization neural network with an online adaptive algorithm for real-time beamforming in dynamic cell-free wireless systems, significantly improving sum rate and computational efficiency.

Contribution

The paper proposes HGNet and OAU algorithms for adaptive beamforming, enabling real-time operation in changing environments with theoretical error bounds.

Findings

Achieves higher sum rate in dynamic environments.

Operates with low latency of milliseconds.

Reduces computational cost compared to traditional methods.

Abstract

In this paper, we consider real-time beamforming design for dynamic wireless environments with varying channels and different numbers of access points (APs) and users in cell-free systems. Specifically, a sum-rate maximization optimization problem is formulated for the beamforming design in dynamic wireless environments of cell-free systems. To efficiently solve it, a high-generalization network (HGNet) is proposed to adapt to the changing numbers of APs and users. Then, a high-generalization beamforming module is also designed in HGNet to extract the valuable features for the varying channels, and we theoretically prove that such a high-generalization beamforming module is able to reduce the upper bound of the generalization error. Subsequently, by online adaptively updating about 3% of the parameters of HGNet, an online adaptive updating (OAU) algorithm is proposed to enable the online adaptive real-time beamforming design for improving the sum rate. Numerical results demonstrate that the proposed HGNet with OAU algorithm achieves a higher sum rate with a lower computational cost on the order of milliseconds, thus realizing the real-time beamforming design for dynamic wireless environments in cell-free systems.