在线学习辅助的智能接收机设计与实现

doi:10.11959/j.issn.1000-436x.2024012

Abstract

Abstract:

To address the issue of reliable communication under complicated scenarios, an online learning-assisted intelligent OFDM receiver was proposed.The variations of the channel environment could be precepted by the receiver, and the optimal parameters of the receiver under the current scenario were obtained by collecting data and training online.In the channel estimation module of the OFDM system, a performance comparator based on the mean square error of noisy channel samples was designed as the indicator of channel environment variations.To accelerate the online training progress, a lightweight neural network structure was applied.The proposed method was further implemented and verified based on universal software radio peripherals.The numerical simulation and over-the-air experimental results demonstrate that the proposed receiver can perceive and adapt to new environments effectively, and outperforms existing machine learning methods in terms of receiving performance and convergence rate with a limited number of pilots.

Key words: machine learning, intelligent receiver, online training, OFDM

CLC Number:

TN929.5

Lingjin KONG, Kai MEI, Xiaoran LIU, Jun XIONG, Haitao ZHAO, Jibo WEI. Design and implementation of online learning assisted intelligent receiver[J]. Journal on Communications, 2024, 45(1): 18-30.

Figures/Tables 16

References 32

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方法	网络层	数据维度	激活函数
Online CE	输入（LS估计）	144	无
	输出	288
SwitchNet	输入（LS估计）	144	无
	FC₁、FC₂、FC₃	288、288、288
	输出α₁FC₁+α₂FC₂+	288
	α₃FC₃
TransferNet	输入（LS估计）	144	无
	FC	288
	输出	288

参数	参数值
SNR/dB	5~25
损失函数	MSE
初始化学习率	1×10⁴
优化器	Adam
训练轮次	100
训练数据量/组	15 000

模型	Model A	Model B	Model C	Model D	Model E	Model F
Model A	0	0	0	0	0	0
Model B	1	0	0	0	0	0
Model C	1	0.994 5	0	0	0	0
Model D	1	1	0.634 2	0	0	0
Model E	1	1	1	1	0	0
Model F	1	1	1	1	0	0

Design and implementation of online learning assisted intelligent receiver

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