Polynomial-Expansion Multi-Cell Aware Detector for Uplink Massive MIMO Systems with Imperfect CSI

Abstract

In this paper, we propose a multi-cell aware (MCA) detector for uplink multi-cell massive multiple-input multiple-output (MIMO) systems. The proposed detector exploits knowledge of the channel statistics but data exchange between different base stations over backhaul links is not required. A correlated channel model is considered and the adopted channel state information (CSI) acquisition model includes the effects of estimation errors and pilot contamination. In contrast to the conventional minimum mean square error (MMSE) detector, which mitigates only the multiple-access interference (MAI) in the target cell, the proposed detector takes the interference from neighboring cells and pilot contamination into account and therefore achieves substantially higher sum rates. Moreover, in order to reduce the computational complexity, the matrix inversion required for the MCA detector is approximated by a matrix polynomial leading to a new polynomial-expansion MCA (PEMCA) detector. Using results from random matrix theory, we derive closed-form expressions for the optimal coefficients of the matrix polynomial, which only depend on the channel statistics but not on the channel realizations. Our simulation results show that the PEMCA detector with only a few terms in the matrix polynomial achieves a considerably higher sum rate than the conventional MMSE detector while having a lower computational complexity.