Han, Yu, Wang, Zhenyong, Guo, Qing, and Xiang, Wei (2020) Deep learning-based detection for moderate-density code multiple access in IoT networks. IEEE Communications Letters, 24 (1). pp. 122-125.

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Abstract

In the era of the Internet of Things, massive devices communications become a fundamental problem. To improve spectral efficiency and reduce latency, a new non-orthogonal multiple access scheme dubbed moderate-density code multiple access (MCMA) is presented. We also propose a new deep learning-based multi-user detection algorithm for MCMA systems, which is based upon a new graphic representation of the Tanner graph for the message passing algorithm (MPA). The proposed algorithm learns to adjust the weights of the edges of the neural network to realize multi-user detection without iterations as required in conventional MPA algorithms. Experimental results show that with an increase in the overloading factor and the number of users, the BER performance of the proposed scheme is better than that of deep learning-aided SCMA (DL-SCMA) with a lower computational complexity.

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