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

系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (1): 12-21.doi: 10.12305/j.issn.1001-506X.2025.01.02

基于神经网络的全双工干扰机自干扰对消方法

朱虹宇¹^,², 胡卫东¹^,², 王超¹^,*, 施庆展¹, 张曦蒙¹^,², 袁乃昌¹^,²

1. 国防科技大学电子科学学院, 湖南长沙 410003
2. 国防科技大学自动目标识别国家重点实验室, 湖南长沙 410003

收稿日期:2023-10-17 出版日期:2025-01-21 发布日期:2025-01-25
通讯作者: 王超
作者简介:朱虹宇(1994—), 男, 博士研究生, 主要研究方向为全双工
胡卫东(1967—), 男, 教授, 博士, 主要研究方向为目标识别、复杂波形设计
王超(1977—), 男, 副教授, 博士, 主要研究方向为电子对抗
施庆展(1990—), 男, 讲师, 博士, 主要研究方向为雷达信号处理、电子对抗
张曦蒙(1995—), 男, 博士研究生, 主要研究方向为组网通信目标的定位与跟踪
袁乃昌(1965—), 男, 教授, 博士, 主要研究方向为复杂电磁环境电子对抗
基金资助:
国家自然科学基金(62301570)

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

Hongyu ZHU¹^,², Weidong HU¹^,², Chao WANG¹^,*, Qingzhan SHI¹, Ximeng ZHANG¹^,², Naichang YUAN¹^,²

1. College of Electronic Science and Technology, National University of Defense Technology, Changsha 410003, China
2. National Key Laboratory of Automatic Target Recognition, National University of Defense Technology, Changsha 410003, China

Received:2023-10-17 Online:2025-01-21 Published:2025-01-25
Contact: Chao WANG

摘要/Abstract

摘要：

针对复杂而强的非线性自干扰信号的对消问题，分析全双工系统中发射泄露产生的自干扰信号的组成及其非线性特性，基于多级隔离和对消自干扰的思想，提出一种数字域神经网络方法。神经网络通过快速学习和感知侦收自干扰信道模型参数，能有效地对消泄露进来的自干扰信号。仿真实验结果表明，与传统的线性对消和数字对消方法相比，所提方法能够快速适应雷达信号的变化，具有更强的自干扰抑制能力，为改善全双工干扰机的性能提供了技术途径。

关键词: 全双工干扰机, 大功率信号, 非线性特性, 雷达信号变化, 自干扰抑制能力

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

中图分类号:

TN974

朱虹宇, 胡卫东, 王超, 施庆展, 张曦蒙, 袁乃昌. 基于神经网络的全双工干扰机自干扰对消方法[J]. 系统工程与电子技术, 2025, 47(1): 12-21.

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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参考文献 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

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基于神经网络的全双工干扰机自干扰对消方法

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

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