Centralized and Distributed Power Allocation for Max-Min Fairness in Cell-Free Massive MIMO

Abstract

Cell-free Massive MIMO systems consist of a large number of geographically distributed access points (APs) that serve users by coherent joint transmission. Downlink power allocation is important in these systems, to determine which APs should transmit to which users and with what power. If the system is implemented correctly, it can deliver a more uniform user performance than conventional cellular networks. To this end, previous works have shown how to perform system-wide max-min fairness power allocation when using maximum ratio precoding. In this paper, we first generalize this method to arbitrary precoding, and then train a neural network to perform approximately the same power allocation but with reduced computational complexity. Finally, we train one neural network per AP to mimic system-wide max-min fairness power allocation, but using only local information. By learning the structure of the local propagation environment, this method outperforms the state-of-the-art distributed power allocation method from the Cell-free Massive MIMO literature.

arXiv commentPublished at 2019 Asilomar Conference on Signals, Systems, and Computers, 5 pages, 3 figures

References in corpus (6)

• Scalable Cell-Free Massive MIMO Systems
• Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management
• Ubiquitous Cell-Free Massive MIMO Communications
• Scalability Aspects of Cell-Free Massive MIMO
• On the Performance of Cell-Free Massive MIMO with Short-Term Power Constraints
• Performance of Cell-Free Massive MIMO Systems with MMSE and LSFD Receivers

Cited by in corpus (4)

• Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems
• A Survey on User-Centric Cell-Free Massive MIMO Systems
• ADMM for Downlink Beamforming in Cell-Free Massive MIMO Systems
• Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning