Study of Block Diagonalization Precoding and Power Allocation for Multiple-Antenna Systems with Coarsely Quantized Signals

TL;DR

This paper introduces novel block diagonalization and power allocation algorithms tailored for large-scale multi-antenna systems with low-resolution, coarsely quantized signals, aiming to enhance sum-rate performance and reduce complexity.

Contribution

It proposes Coarse Quantization-Aware precoding and power allocation algorithms that mitigate quantization effects using Bussgang decomposition, a novel approach in this context.

Findings

Proposed algorithms improve sum-rate performance with low-resolution signals.

Analyses show reduced computational complexity and power consumption.

Simulation results demonstrate superior performance over existing methods.

Abstract

In this work, we present block diagonalization and power allocation algorithms for large-scale multiple-antenna systems with coarsely quantized signals. In particular, we develop Coarse Quantization-Aware Block Diagonalization ${\scriptstyle\mathrm{\left(CQA-BD\right)}}$ and Coarse Quantization-Aware Regularized Block Diagonalization ${\scriptstyle\mathrm{\left(CQA-RBD\right)}}$ precoding algorithms that employ the Bussgang decomposition and can mitigate the effects of low-resolution signals and interference. Moreover, we also devise the Coarse Quantization-Aware Most Advantageous Allocation Strategy ${\scriptstyle\mathrm{\left(CQA-MAAS\right)}}$ power allocation algorithm to improve the sum rate of precoders that operate with low-resolution signals. An analysis of the sum-rate performance is carried out along with computational complexity and power consumption studies of the proposed and existing techniques. Simulation results illustrate the performance of the proposed ${\scriptstyle\mathrm{CQA-BD}}$ and ${\scriptstyle\mathrm{CQA-RBD}}$ precoding algorithms, and the proposed ${\scriptstyle\mathrm{CQA-MAAS}}$ power allocation strategy against existing approaches.