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

• DOI: 10.48550/arxiv.2409.13286
• Publication Date: 2024-12-01
• Authors: Cheng Zhang; Shuangbo Xiong; Mengqing He; Lan Wei; Yongming Huang; Wei Zhang
• Keywords: Signal Processing (eess.SP); FOS: Computer and information sciences; Computer Science - Information Theory; Information Theory (cs.IT); FOS: Electrical engineering, electronic engineering, information engineering; Electrical Engineering and Systems Science - Signal Processing

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.