Stochastic Hybrid Combining Design for Quantized Massive MIMO Systems

TL;DR

This paper introduces a stochastic hybrid combining scheme for quantized massive MIMO systems that adapts to long-term channel statistics, reducing power consumption and improving performance despite coarse quantization and imperfect channel information.

Contribution

It proposes a novel stochastic hybrid analog-digital combiner and a relaxed stochastic successive convex approximation algorithm for optimizing power and rate in quantized mMIMO systems.

Findings

Proposed scheme outperforms benchmark methods in simulations.

Joint optimization reduces transmit power under rate constraints.

Adapts to long-term channel statistics for improved robustness.

Abstract

Both the power-dissipation and cost of massive multiple-input multiple-output (mMIMO) systems may be substantially reduced by using low-resolution analog-to-digital converters (LADCs) at the receivers. However, both the coarse quantization of LADCs and the inaccurate instantaneous channel state information (ICSI) degrade the performance of quantized mMIMO systems. To overcome these challenges, we propose a novel stochastic hybrid analog-digital combiner (SHC) scheme for adapting the hybrid combiner to the long-term statistics of the channel state information (SCSI). We seek to minimize the transmit power by jointly optimizing the SHC subject to average rate constraints. For the sake of solving the resultant nonconvex stochastic optimization problem, we develop a relaxed stochastic successive convex approximation (RSSCA) algorithm. Simulations are carried out to confirm the benefits of our proposed scheme over the benchmarkers.