Data-aided Active User Detection with False Alarm Correction in Grant-Free Transmission

TL;DR

This paper introduces a two-step data-aided active user detection scheme with false alarm correction for grant-free transmission, significantly improving detection accuracy and data decoding performance over traditional compressed sensing methods.

Contribution

It proposes a novel two-step scheme combining LDS-based preamble pool, message passing algorithms, and false alarm correction to enhance active user detection and data decoding in grant-free systems.

Findings

Detection accuracy improved by over 1.5 dB

Data decoding performance significantly enhanced

False alarm rate reduced compared to traditional methods

Abstract

In most existing grant-free (GF) studies, the two key tasks, namely active user detection (AUD) and payload data decoding, are handled separately. In this paper, a two-step dataaided AUD scheme is proposed, namely the initial AUD step and the false alarm correction step respectively. To implement the initial AUD step, an embedded low-density-signature (LDS) based preamble pool is constructed. In addition, two message passing algorithm (MPA) based initial estimators are developed. In the false alarm correction step, a redundant factor graph is constructed based on the initial active user set, on which MPA is employed for data decoding. The remaining false detected inactive users will be further recognized by the false alarm corrector with the aid of decoded data symbols. Simulation results reveal that both the data decoding performance and the AUD performance are significantly enhanced by more than 1:5 dB at the target accuracy of 10^3 compared with the traditional compressed sensing (CS) based counterparts