Uplink NOMA signal transmission with convolutional neural networks approach  

作　　者：LIN Chuan CHANG Qing LI Xianxu 

机构地区：[1]School of Electronic and Information Engineering,Beihang University,Beijing 100191,China [2]State Grid Information and Telecommunication Branch,Beijing 100761,China

出　　处：《Journal of Systems Engineering and Electronics》2020年第5期890-898,共9页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China (61471021)。

摘　　要：Non-orthogonal multiple access(NOMA), featuring high spectrum efficiency, massive connectivity and low latency, holds immense potential to be a novel multi-access technique in fifth-generation(5G) communication. Successive interference cancellation(SIC) is proved to be an effective method to detect the NOMA signal by ordering the power of received signals and then decoding them. However, the error accumulation effect referred to as error propagation is an inevitable problem. In this paper,we propose a convolutional neural networks(CNNs) approach to restore the desired signal impaired by the multiple input multiple output(MIMO) channel. Especially in the uplink NOMA scenario,the proposed method can decode multiple users' information in a cluster instantaneously without any traditional communication signal processing steps. Simulation experiments are conducted in the Rayleigh channel and the results demonstrate that the error performance of the proposed learning system outperforms that of the classic SIC detection. Consequently, deep learning has disruptive potential to replace the conventional signal detection method.

关 键 词：non-orthogonal multiple access(NOMA) deep learning(DL) convolutional neural networks(CNNs) signal detection 

分 类 号：TN929.5[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程]