Uplink NOMA signal transmission with convolutional neural networks approach

• 作者: CHANG Qing LI Xianxu LIN Chuan
• 来源期刊: 系统工程与电子技术（英文版）
• 年，卷（期）: 2020,（5）
• 所属期刊栏目: ELECTRONICS TECHNOLOGY
• 页码范围: 890-898
• 页数: 9页
• 语种: 英文
• DOI: 10.23919/JSEE.2020.000068

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.

作者信息

序号	姓名	单位	发文数	被引次数	H指数	G指数
1	LIN Chuan		1	0	0.0	0.0
2	CHANG Qing		2	16	1.0	2.0
3	LI Xianxu		1	0	0.0	0.0