Uplink Performance of Time-Reversal MRC in Massive MIMO Systems Subject to Phase Noise

TL;DR

This paper investigates how oscillator phase noise affects the uplink performance of Massive MIMO systems using time-reversal MRC, deriving bounds on capacity and analyzing the impact on system design.

Contribution

It provides a comprehensive analysis of phase noise effects in Massive MIMO with imperfect CSI, comparing synchronous and non-synchronous operation modes, and offers practical insights for system optimization.

Findings

An $O(\sqrt{M})$ array gain is achievable with the proposed processing.

Phase noise limits the data transmission interval and the number of users.

Derived lower bounds on sum-capacity for both operation modes.

Abstract

Multi-user multiple-input multiple-output (MU-MIMO) cellular systems with an excess of base station (BS) antennas (Massive MIMO) offer unprecedented multiplexing gains and radiated energy efficiency. Oscillator phase noise is introduced in the transmitter and receiver radio frequency chains and severely degrades the performance of communication systems. We study the effect of oscillator phase noise in frequency-selective Massive MIMO systems with imperfect channel state information (CSI). In particular, we consider two distinct operation modes, namely when the phase noise processes at the $M$ BS antennas are identical (synchronous operation) and when they are independent (non-synchronous operation). We analyze a linear and low-complexity time-reversal maximum-ratio combining (TR-MRC) reception strategy. For both operation modes we derive a lower bound on the sum-capacity and we compare their performance. Based on the derived achievable sum-rates, we show that with the proposed receive processing an $O(\sqrt{M})$ array gain is achievable. Due to the phase noise drift the estimated effective channel becomes progressively outdated. Therefore, phase noise effectively limits the length of the interval used for data transmission and the number of scheduled users. The derived achievable rates provide insights into the optimum choice of the data interval length and the number of scheduled users.