Data-aided Active User Detection with False Alarm Correction in Grant-Free Transmission
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
Cite
@article{arxiv.2207.12830,
title = {Data-aided Active User Detection with False Alarm Correction in Grant-Free Transmission},
author = {Linjie Yang and Pingzhi Fan and Des McLernon and Li X Zhang},
journal= {arXiv preprint arXiv:2207.12830},
year = {2022}
}