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Open AccessArticle

A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection

by Chuan Lin 1, Qing Chang 1,* and Xianxu Li 2
1
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
2
State Grid Information & Telecommunication Branch, Beijing 100761, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(11), 2526; https://doi.org/10.3390/s19112526
Received: 7 April 2019 / Revised: 29 May 2019 / Accepted: 31 May 2019 / Published: 2 June 2019
(This article belongs to the Section Sensor Networks)
As a key candidate technique for fifth-generation (5G) mobile communication systems, non-orthogonal multiple access (NOMA) has attracted considerable attention in the field of wireless communication. Successive interference cancellation (SIC) is the main NOMA detection method applied at receivers for both uplink and downlink NOMA transmissions. However, SIC is limited by the receiver complex and error propagation problems. Toward this end, we explore a high-performance, high-efficiency tool—deep learning (DL). In this paper, we propose a learning method that automatically analyzes the channel state information (CSI) of the communication system and detects the original transmit sequences. In contrast to existing SIC schemes, which must search for the optimal order of the channel gain and remove the signal with higher power allocation factor while detecting a signal with a lower power allocation factor, the proposed deep learning method can combine the channel estimation process with recovery of the desired signal suffering from channel distortion and multiuser signal superposition. Extensive performance simulations were conducted for the proposed MIMO-NOMA-DL system, and the results were compared with those of the conventional SIC method. According to our simulation results, the deep learning method can successfully address channel impairment and achieve good detection performance. In contrast to implementing well-designed detection algorithms, MIMO-NOMA-DL searches for the optimal solution via a neural network (NN). Consequently, deep learning is a powerful and effective tool for NOMA signal detection. View Full-Text
Keywords: 5G; non-orthogonal multiple access (NOMA); multiple-input multiple-output (MIMO); deep learning (DL); wireless communication 5G; non-orthogonal multiple access (NOMA); multiple-input multiple-output (MIMO); deep learning (DL); wireless communication
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Lin, C.; Chang, Q.; Li, X. A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection. Sensors 2019, 19, 2526.

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