Study of Adaptive Activity-Aware Constellation List-Based Detection for Massive Machine-Type Communications

TL;DR

This paper introduces an adaptive activity-aware constellation list-based detector with an $l_0$-norm regularized recursive least-squares algorithm, improving detection accuracy in massive machine-type communications by mitigating error propagation.

Contribution

It presents a novel adaptive detector that uses constellation-based list strategies and regularized recursive least-squares, enhancing detection performance in massive MTC scenarios.

Findings

Successfully mitigates error propagation.

Approaches the performance of oracle LMMSE.

Requires only pilot symbols for operation.

Abstract

In this work, we propose an adaptive list-based decision feedback detector along with an $l_0$-norm regularized recursive least-squares algorithm that only requires pilot symbols (AA-CL-DF). The proposed detector employs a list strategy based on the signal constellation points to generate different candidates for detection. Simulation results show that the proposed AA-CL-DF successfully mitigates the error propagation and approaches the performance for the oracle LMMSE algorithm.