Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning
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Abstract (arXiv)
Cell-free massive MIMO (CF-mMIMO) systems represent a promising approach to increase the spectral efficiency of wireless communication systems. However, near-optimal beamforming solutions require a large amount of signaling exchange between access points (APs) and the network controller (NC). In this letter, we propose two unsupervised deep neural networks (DNN) architectures, fully and partially distributed, that can perform decentralized coordinated beamforming with zero or limited communication overhead between APs and NC, for both fully digital and hybrid precoding. The proposed DNNs achieve near-optimal sum-rate while also reducing computational complexity by 10-24x compared to conventional near-optimal solutions.
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References · 5
- 2019DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applicationscited by 121
- 2019Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precodingcited by 13
- 2019Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementationcited by 13
- 2020Unsupervised Deep Learning for Massive MIMO Hybrid Beamformingcited by 10
- 2020RSSI-Based Hybrid Beamforming Design with Deep Learningcited by 2
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