基于Res-DNN的端到端MIMO系统信号检测算法

doi:10.11959/j.issn.2096-3750.2022.00256

Abstract

Abstract:

Deep learning can improve the effect of signal detection by extracting the inherent characteristics of wireless communication data.To solve the tradeoff between the performance and complexity of MIMO system signal detection, an end-to-end MIMO system signal detection scheme based on deep learning was proposed.The encoder and the decoder based on residual deep neural network replace the transmitter and the receiver of the wireless communication system respectively, and they were trained in an end-to-end manner as a whole.Firstly, the features of the input data were extracted by encoder, then the communication model was established and was sent to the zero forcing detector for preliminary detection.Finally, the detection signal was reconstructed through the decoder.Simulation results show that the proposed detection scheme is superior to the same type of algorithm, and the detection performance is significantly better than that of the MMSE detection algorithm at the expense of a certain time complexity.

Key words: deep learning, MIMO system, signal detection, Res-DNN, end-to-end

CLC Number:

TN92

Guoquan LI, Yonghai XU, Jinzhao LIN, Zhengwen HUANG. Res-DNN based signal detection algorithm for end-to-end MIMO systems[J]. Chinese Journal on Internet of Things, 2022, 6(1): 65-72.

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References 12

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类型	参数配置
天线数目	8×8/16×16
信道类型	瑞利信道
调制方式	BPSK/QPSK
训练数据的信噪比	15 dB
神经网络层数	11层
训练次数	20 000次
神经元数目	128个
激活函数	ReLU
初始学习率	0.000 3
损失函数	二次代价函数
优化器	Adam

检测算法	计算时间/s
ZF检测	0.000 025 1
MMSE检测	0.000 049 8
ML检测	0.525 025 8
DetNet检测	0.000 281 7
所提方案	0.000 451 4

Res-DNN based signal detection algorithm for end-to-end MIMO systems

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