Massive MIMO relaying with linear precoding in correlated channels under limited feedback

TL;DR

This paper analyzes massive MIMO relay systems with linear precoding under imperfect channel information and channel correlation, deriving deterministic equivalents for SINR using large random matrix theory.

Contribution

It provides a novel deterministic equivalent analysis of SINR in massive MIMO relay systems with imperfect CSIT and channel correlation, including closed-form expressions.

Findings

Deterministic equivalent of SINR matches simulations.

Closed-form expressions for key channel metrics.

Accurate predictions for large-scale MIMO relay performance.

Abstract

In this paper we study on a massive MIMO relay system with linear precoding under the conditions of imperfect channel state information at the transmitter (CSIT) and per-user channel transmit correlation. In our system the source-relay channels are massive multiple-input multiple-output (MIMO) ones and the relay-destination channels are massive multiple-input single-output (MISO) ones. Large random matrix theory (RMT) is used to derive a deterministic equivalent of the signal-to-interference-plus-noise ratio (SINR) at each user in massive MIMO amplify-forward and decode-forward (M-MIMO-ADF) relaying with regularized zero-forcing (RZF) precoding, as the number of transmit antennas and users M,K approaches to infinity and M>>K. In this paper we obtain a closed-form expression for the deterministic equivalent of h^H_kW(hat)_lh(hat)_k, and we give two theorems and a corollary to derive the deterministic equivalent of the SINR at each user. Simulation results show that the deterministic equivalent of the SINR at each user in M-MIMO-ADF relaying and the results of Theorem 1, Theorem 2, Proposition 1 and Corollary 1 are accurate.