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

TL;DR

This paper introduces the bit-metric decoding rate (BMDR) as a new metric for MU-MIMO systems with non-linear detectors, enabling better link adaptation and PHY abstraction through machine learning predictions.

Contribution

It proposes BMDR as an equivalent of post-equalization SINR for non-linear MU-MIMO detectors and develops a machine learning method to predict it.

Findings

BMDR effectively characterizes detector performance.

Machine learning accurately predicts BMDR from system parameters.

The approach improves link adaptation in MU-MIMO systems.

Abstract

Link-adaptation (LA) is one of the most important aspects of wireless communications where the modulation and coding scheme (MCS) used by the transmitter is adapted to the channel conditions in order to meet a certain target error-rate. In a single-user SISO (SU-SISO) system with out-of-cell interference, LA is performed by computing the post-equalization signal-to-interference-noise ratio (SINR) at the receiver. The same technique can be employed in multi-user MIMO (MU-MIMO) receivers that use linear detectors. Another important use of post-equalization SINR is for physical layer (PHY) abstraction, where several PHY blocks like the channel encoder, the detector, and the channel decoder are replaced by an abstraction model in order to speed up system-level simulations. However, for MU-MIMO systems with non-linear receivers, there is no known equivalent of post-equalization SINR which makes both LA and PHY abstraction extremely challenging. This important issue is addressed in this two-part paper. In this part, a metric called the bit-metric decoding rate (BMDR) of a detector, which is the proposed equivalent of post-equalization SINR, is presented. Since BMDR does not have a closed form expression that would enable its instantaneous calculation, a machine-learning approach to predict it is presented along with extensive simulation results.