Adaptive Reduced-Rank Minimum Symbol-Error-Rate Receive Processing for Large-Scale Multiple-Antenna Systems

TL;DR

This paper introduces an adaptive reduced-rank receive processing method for large-scale MIMO systems that minimizes symbol error rate using joint preprocessing, decimation, and filtering, improving performance and reducing complexity.

Contribution

It proposes a novel joint preprocessing, decimation, and filtering framework with stochastic gradient algorithms for large-scale MIMO, enhancing SER performance with lower computational cost.

Findings

Outperforms existing methods in SER in simulations

Achieves lower complexity compared to traditional approaches

Effective in time-varying wireless environments

Abstract

In this work, we propose a novel adaptive reduced-rank receive processing strategy based on joint preprocessing, decimation and filtering (JPDF) for large-scale multiple-antenna systems. In this scheme, a reduced-rank framework is employed for linear receive processing and multiuser interference suppression based on the minimization of the symbol-error-rate (SER) cost function. We present a structure with multiple processing branches that performs a dimensionality reduction, where each branch contains a group of jointly optimized preprocessing and decimation units, followed by a linear receive filter. We then develop stochastic gradient (SG) algorithms to compute the parameters of the preprocessing and receive filters, along with a low-complexity decimation technique for both binary phase shift keying (BPSK) and $M$-ary quadrature amplitude modulation (QAM) symbols. In addition, an automatic parameter selection scheme is proposed to further improve the convergence performance of the proposed reduced-rank algorithms. Simulation results are presented for time-varying wireless environments and show that the proposed JPDF minimum-SER receive processing strategy and algorithms achieve a superior performance than existing methods with a reduced computational complexity.