Adaptive Reduced-Rank MBER Linear Receive Processing for Large Multiuser MIMO Systems

TL;DR

This paper introduces an adaptive reduced-rank linear receive processing method for large multiuser MIMO systems that minimizes BER, using joint interpolation, decimation, and filtering to improve performance and reduce complexity.

Contribution

It proposes a novel joint interpolation, decimation, and filtering framework with stochastic gradient algorithms for adaptive reduced-rank MIMO detection.

Findings

Achieves superior BER performance compared to existing methods.

Reduces computational complexity in large MIMO systems.

Effective in time-varying environments.

Abstract

In this work, we propose a novel adaptive reduced-rank strategy based on joint interpolation, decimation and filtering (JIDF) for large multiuser multiple-input multiple-output (MIMO) systems. In this scheme, a reduced-rank framework is proposed for linear receive processing and multiuser interference suppression according to the minimization of the bit error rate (BER) cost function. We present a structure with multiple processing branches that performs dimensionality reduction, where each branch contains a group of jointly optimized interpolation and decimation units, followed by a linear receive filter. We then develop stochastic gradient (SG) algorithms to compute the parameters of the interpolation and receive filters along with a low-complexity decimation technique. Simulation results are presented for time-varying environments and show that the proposed MBER-JIDF receive processing strategy and algorithms achieve a superior performance to existing methods at a reduced complexity.