基于神经网络的全双工干扰机自干扰对消方法

doi:10.12305/j.issn.1001-506X.2025.01.02

Abstract

Abstract:

In view of the problem of cancellation the complex and strong nonlinear self-interference signals, the paper analyses the composition and non-linear characteristics of the self-interference signals generated by the leakage in full-duplex systems. And a digital domain neural network approach is proposed based on the idea of multi-level isolation and self-interference cancellation. The neural network quickly learns and perceives the parameters of the self-interference channel model, effectively canceling the leaked self-interference signals. Simulation results show that compared to traditional linear cancellation and digital cancellation methods, the proposed approach can rapidly adapt to changes in radar signals, exhibiting stronger self-interference suppression capability and providing a technological pathway for enhancing the performance of full-duplex jammer.

Key words: full-duplex jammer, high-power signal, non-linear characteristics, changes in radar signals, self-interference suppression capability

CLC Number:

TN974

Hongyu ZHU, Weidong HU, Chao WANG, Qingzhan SHI, Ximeng ZHANG, Naichang YUAN. Neural network-based self-interference cancellation approach for full-duplex jammer[J]. Systems Engineering and Electronics, 2025, 47(1): 12-21.

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References 34

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参数	取值
载频频率/GHz	10
带宽/GHz	1
无线信道长度	4
K/dB	30
主通道延迟/ns	2
IRR/dB	30
PN 3 dB带宽/Hz	50
PA平滑参数	3
回退功率/dB	0
接收机底噪/dBm	-100

方法	内容
base(传播域+模拟域)	天线分离+单抽头
LS(数字域, 48 ms)	传统建模与模型求解
AI1(数字域, 31.9 ms)	神经网络(AI1)
AI2(数字域, 23.9 ms)	神经网络(AI2)

方法名	内容	训练消耗时间/ms	剩余自干扰重建时间/ms	自干扰对消性能/dB
方法1	LS	0.58	0.26	4.75
方法2	LS(无多径效应)	0.25	0.12	3.13
方法3	LS(无非线性效应)	0.23	0.09	6.05
方法4	LS(仅线性)	0.37	0.14	3.12
方法5	AI1	1 875	339	3.21
方法6	AI2(考虑多径效应)	983.9	72.6	7.29

Neural network-based self-interference cancellation approach for full-duplex jammer

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