Local Partial Zero-Forcing Precoding for Cell-Free Massive MIMO

Abstract

Cell-free Massive MIMO (multiple-input multiple-output) is a promising distributed network architecture for 5G-and-beyond systems. It guarantees ubiquitous coverage at high spectral efficiency (SE) by leveraging signal co-processing at multiple access points (APs), aggressive spatial user multiplexing and extraordinary macro-diversity gain. In this study, we propose two distributed precoding schemes, referred to as \textit{local partial zero-forcing} (PZF) and \textit{local protective partial zero-forcing} (PPZF), that further improve the spectral efficiency by providing an adaptable trade-off between interference cancelation and boosting of the desired signal, with no additional front-hauling overhead, and implementable by APs with very few antennas. We derive closed-form expressions for the achievable SE under the assumption of independent Rayleigh fading channel, channel estimation error and pilot contamination. PZF and PPZF can substantially outperform maximum ratio transmission and zero-forcing, and their performance is comparable to that achieved by regularized zero-forcing (RZF), which is a benchmark in the downlink. Importantly, these closed-form expressions can be employed to devise optimal (long-term) power control strategies that are also suitable for RZF, whose closed-form expression for the SE is not available.

arXiv commentThis paper was accepted for publication in IEEE Transactions on Wireless Communications on March 31, 2020. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses

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• Airplane-Aided Integrated Next-Generation Networking
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• Decentralized Beamforming Design for Intelligent Reflecting Surface-enhanced Cell-free Networks
• Iterative Access Point Selection, MMSE Precoding and Power Allocation for Cell-Free Networks
• Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning
• Robust MMSE Precoding and Power Allocation for Cell-Free Networks