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Uplink NOMA signal transmission with convolutional neural networks approach
Journal of Systems Engineering and Electronics ( IF 2.1 ) Pub Date : 2020-10-01 , DOI: 10.23919/jsee.2020.000068
Lin Chuan , Chang Qing , Li Xianxu

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.

中文翻译:

使用卷积神经网络方法进行上行 NOMA 信号传输

非正交多址(NOMA)具有频谱效率高、连接性大、时延低等特点,具有成为第五代(5G)通信中新型多址技术的巨大潜力。连续干扰消除(SIC)被证明是一种通过对接收信号的功率进行排序然后对其进行解码来检测NOMA信号的有效方法。然而,被称为错误传播的错误累积效应是一个不可避免的问题。在本文中,我们提出了一种卷积神经网络 (CNN) 方法来恢复受多输入多输出 (MIMO) 通道影响的所需信号。特别是在上行 NOMA 场景中,所提出的方法可以在没有任何传统通信信号处理步骤的情况下即时解码集群中的多个用户信息。在瑞利通道中进行了仿真实验,结果表明所提出的学习系统的错误性能优于经典的 SIC 检测。因此,深度学习具有取代传统信号检测方法的颠覆性潜力。
更新日期:2020-10-01
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