Optimal Large-MIMO Data Detection with Transmit Impairments

TL;DR

This paper introduces LAMA-I, a novel large-MIMO data detection algorithm that accounts for transmit impairments, achieving near-optimal error rates with low complexity in practical systems.

Contribution

It presents a new detection algorithm that models transmit impairments and demonstrates near-IO performance with reduced computational complexity.

Findings

LAMA-I achieves near-IO error-rate performance in large-MIMO systems.

The algorithm accounts for hardware impairments like amplifier non-linearities and phase noise.

Conditions are provided under which LAMA-I is optimal in the large-system limit.

Abstract

Real-world transceiver designs for multiple-input multiple-output (MIMO) wireless communication systems are affected by a number of hardware impairments that already appear at the transmit side, such as amplifier non-linearities, quantization artifacts, and phase noise. While such transmit-side impairments are routinely ignored in the data-detection literature, they often limit reliable communication in practical systems. In this paper, we present a novel data-detection algorithm, referred to as large-MIMO approximate message passing with transmit impairments (short LAMA-I), which takes into account a broad range of transmit-side impairments in wireless systems with a large number of transmit and receive antennas. We provide conditions in the large-system limit for which LAMA-I achieves the error-rate performance of the individually-optimal (IO) data detector. We furthermore demonstrate that LAMA-I achieves near-IO performance at low computational complexity in realistic, finite dimensional large-MIMO systems.