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

• URL: http://arxiv.org/abs/2106.11198v1
• Date: Mon, 21 Jun 2021 15:34:14 GMT
• Title: Deep Learning-Based Active User Detection for Grant-free SCMA Systems
• Authors: Thushan Sivalingam, Samad Ali, Nurul Huda Mahmood, Nandana Rajatheva, and Matti Latva-Aho
• Abstract summary: We propose two novel group-based deep neural network active user detection schemes. Schemes learn the nonlinear mapping, i.e., multi-dimensional codebook structure and the channel characteristic. offline pre-trained model is able to detect the active devices without any channel state information.
• Score: 12.565459084483045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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