Deep Learning-Based Power Control for Uplink Cell-Free Massive MIMO Systems

• URL: http://arxiv.org/abs/2110.09001v1
• Date: Mon, 18 Oct 2021 03:48:54 GMT
• Title: Deep Learning-Based Power Control for Uplink Cell-Free Massive MIMO Systems
• Authors: Yongshun Zhang, Jiayi Zhang, Yu Jin, Stefano Buzzi, Bo Ai
• Abstract summary: Instead of using supervised learning, the proposed method relies on unsupervised learning, in which optimal power allocations are not required to be known. A deep neural network (DNN) is trained to learn the map between fading coefficients and power coefficients within short time. It is interesting to note that the spectral efficiency of mMIMO systems with the proposed method outperforms previous optimization methods for max-min optimization.
• Score: 31.06830781747216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, a general framework for deep learning-based power control methods for max-min, max-product and max-sum-rate optimization in uplink cell-free massive multiple-input multiple-output (CF mMIMO) systems is proposed. Instead of using supervised learning, the proposed method relies on unsupervised learning, in which optimal power allocations are not required to be known, and thus has low training complexity. More specifically, a deep neural network (DNN) is trained to learn the map between fading coefficients and power coefficients within short time and with low computational complexity. It is interesting to note that the spectral efficiency of CF mMIMO systems with the proposed method outperforms previous optimization methods for max-min optimization and fits well for both max-sum-rate and max-product optimizations.

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