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

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....

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Veröffentlicht in:IEEE wireless communications letters 2024-09, p.1-1
Hauptverfasser: Zhang, Cheng, Xiong, Shuangbo, He, Mengqing, Wei, Lan, Huang, Yongming, Zhang, Wei
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Xiong, Shuangbo
He, Mengqing
Wei, Lan
Huang, Yongming
Zhang, Wei
description 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.
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subjects Array signal processing
conditional variational auto-encoder (CVAE)
hybrid beamforming
MIMO communication
mixed density network (MDN)
Numerical models
Optimization
probing beam
Radio frequency
Training
title Generative Learning Powered Probing Beam Optimization for Cell-Free Hybrid Beamforming
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