Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM

TL;DR

This paper introduces a quantized coordinate minimization (QCM) algorithm for low-resolution precoding in multi-antenna downlink channels with OFDM, demonstrating high information rates and low complexity compared to existing methods.

Contribution

The paper proposes a novel QCM precoding algorithm optimized for low-resolution signaling in multi-antenna OFDM systems, with comprehensive performance comparisons.

Findings

QCM achieves the highest information rates among tested algorithms.

QCM has the lowest computational complexity in terms of multiplications.

Performance degrades similarly to zero-forcing precoding under imperfect channel knowledge.

Abstract

Downlink precoding is considered for multi-path multi-input single-output channels where the base station uses orthogonal frequency-division multiplexing and low-resolution signaling. A quantized coordinate minimization (QCM) algorithm is proposed and its performance is compared to other precoding algorithms including squared infinity-norm relaxation (SQUID), multi-antenna greedy iterative quantization (MAGIQ), and maximum safety margin precoding. MAGIQ and QCM achieve the highest information rates and QCM has the lowest complexity measured in the number of multiplications. The information rates are computed for pilot-aided channel estimation and data-aided channel estimation. Bit error rates for a 5G low-density parity-check code confirm the information-theoretic calculations. Simulations with imperfect channel knowledge at the transmitter show that the performance of QCM and SQUID degrades in a similar fashion as zero-forcing precoding with high resolution quantizers.