Generative Learning Powered Probing Beam Optimization for Cell-Free Hybrid Beamforming

TL;DR

This paper introduces a novel probing beam optimization framework for cell-free MIMO systems, utilizing advanced generative models and collaborative modules to enhance beam measurement and optimization accuracy.

Contribution

It proposes a new PBM augmentation model combining CVAE and mixture density networks with Cholesky decomposition for improved stability and performance in hybrid beamforming.

Findings

Enhanced PBM augmentation performance over traditional CVAE

Effective sum-rate prediction and beam optimization

Simulation results confirm improved system performance

Abstract

Probing beam measurement (PBM)-based hybrid beamforming provides a feasible solution for cell-free MIMO. In this letter, we propose a novel probing beam optimization framework where three collaborative modules respectively realize PBM augmentation, sum-rate prediction and probing beam optimization. Specifically, the PBM augmentation model integrates the conditional variational auto-encoder (CVAE) and mixture density networks and adopts correlated PBM distribution with full-covariance, for which a Cholesky-decomposition based training is introduced to address the issues of covariance legality and numerical stability. Simulations verify the better performance of the proposed augmentation model compared to the traditional CVAE and the efficiency of proposed optimization framework.