Abstract
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.
Highlights
Since the concept of non-orthogonal multiple access (NOMA) transmission was proposed, there have been at least three categories of non-orthogonal multiple access: power-domain NOMA, code-domainNOMA, and hybrid-domain NOMA
The application of deep learning in multiple-input multiple-output (MIMO)-NOMA communication systems is a promising approach to address the shortcomings of the successive interference cancellation (SIC) method
Instead of the complicated algorithm design and interference cancellation process, the deep learning approach can search for the optimal solution of the hyperparameters of the multilayer neural network with machine learning
Summary
Since the concept of non-orthogonal multiple access (NOMA) transmission was proposed, there have been at least three categories of non-orthogonal multiple access: power-domain NOMA, code-domain. A scheme integrating DL into an orthogonal frequency division multiplexing (OFDM) system has been put forward [14], and its numerical results revealed the potential performance of DNNs. A fully connected end-to-end DL system including an encoding layer, noise layer, and decoding layer was reported in [15] for MIMO, and [16] designed a long short term memory (LSTM) network for uplink NOMA to realize the end-to-end transmission as well. A fully connected end-to-end DL system including an encoding layer, noise layer, and decoding layer was reported in [15] for MIMO, and [16] designed a long short term memory (LSTM) network for uplink NOMA to realize the end-to-end transmission as well Their results showed the excellent performance of the autoencoder for jointly learning transmit and receive functions. R N × M×K denotes the vector space of all N × M × K real matrices and H (n) is the node-n matricization
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