Bit-Metric Decoding Rate in Multi-User MIMO Systems: Applications

TL;DR

This paper develops algorithms for link adaptation and physical layer abstraction in multi-user MIMO systems with non-linear receivers, using a novel machine learning-based metric called BMDR to improve system performance.

Contribution

It introduces new algorithms for link adaptation, detector selection, and PHY abstraction in MU-MIMO systems utilizing the BMDR metric and machine learning estimation.

Findings

Proposed algorithms outperform existing methods in simulations.

BMDR effectively estimates post-equalization SINR for non-linear detectors.

Machine learning approach accurately predicts BMDR without closed-form expression.

Abstract

This is the second part of a two-part paper that focuses on link-adaptation (LA) and physical layer (PHY) abstraction for multi-user MIMO (MU-MIMO) systems with non-linear receivers. The first part proposes a new metric, called bit-metric decoding rate (BMDR) for a detector, as being the equivalent of post-equalization signal-to-interference-noise ratio (SINR) for non-linear receivers. Since this BMDR does not have a closed form expression, a machine-learning based approach to estimate it effectively is presented. In this part, the concepts developed in the first part are utilized to develop novel algorithms for LA, dynamic detector selection from a list of available detectors, and PHY abstraction in MU-MIMO systems with arbitrary receivers. Extensive simulation results that substantiate the efficacy of the proposed algorithms are presented.