Deep Learning-Based Active User Detection for Grant-free SCMA Systems

TL;DR

This paper introduces two deep learning-based active user detection schemes for grant-free SCMA systems in mMTC, significantly improving detection probability without requiring channel state information.

Contribution

It proposes novel group-based deep neural network schemes that learn nonlinear mappings for active user detection in grant-free SCMA systems, eliminating the need for prior channel knowledge.

Findings

Over twice the detection probability compared to conventional schemes

Effective detection without channel state information

Robust performance with multiple active devices

Abstract

Grant-free random access and uplink non-orthogonal multiple access (NOMA) have been introduced to reduce transmission latency and signaling overhead in massive machine-type communication (mMTC). In this paper, we propose two novel group-based deep neural network active user detection (AUD) schemes for the grant-free sparse code multiple access (SCMA) system in mMTC uplink framework. The proposed AUD schemes learn the nonlinear mapping, i.e., multi-dimensional codebook structure and the channel characteristic. This is accomplished through the received signal which incorporates the sparse structure of device activity with the training dataset. Moreover, the offline pre-trained model is able to detect the active devices without any channel state information and prior knowledge of the device sparsity level. Simulation results show that with several active devices, the proposed schemes obtain more than twice the probability of detection compared to the conventional AUD schemes over the signal to noise ratio range of interest.