电子科技大学学报2024，Vol.53Issue(6)：862-870,9.DOI:10.12178/1001-0548.2024016
基于深度学习的非扩跳频通信体制窄带干扰抑制技术
Deep Learning Based Narrowband Interference Suppression Technique in Non-Spread-Hopping Spectrum Communication System
摘要
Abstract
The wireless service in complex environment shows the trend of data intensive development in recent years,which puts forward higher requirements for the data transmission capability and interference countermeasure capability of wireless communication systems.The existing spread spectrum and frequency hopping communication systems achieve interference countermeasure capability by sacrificing time-frequency resource utilization,but can not meet the demand of high-speed data transmission in complex environment.Therefore,this paper proposes a deep learning based narrowband interference suppression technology of non-spread-hopping spectrum communication system.On the basis of non-spread-hopping spectrum communication system,the frequency-domain notch filtering module and deep neural network module are cascaded at the receiver end to effectively suppress narrowband interference,improving data transmission rate and interference suppression ability at the same time.In particular,the deep neural network is used to reconstruct the expected signal from the distorted signal by the frequency-domain notch filtering module.The experimental results show that the proposed algorithm has a lower bit error rate than the traditional frequency-domain notch filtering algorithms,and the well-trained deep neural network can generalize to the scenarios with differences of signal power,interference power,interference frequency band,interference waveform,etc.关键词
深度学习/泛化能力/窄带干扰抑制/非扩跳频通信Key words
deep learning/generalization ability/narrowband interference suppression/non-spread-hopping spectrum communication分类
信息技术与安全科学引用本文复制引用
马松,王家豪,张蔺,王军..基于深度学习的非扩跳频通信体制窄带干扰抑制技术[J].电子科技大学学报,2024,53(6):862-870,9.基金项目
电子科技大学通信抗干扰全国重点实验室基础科研创新基金(IFN20230102) （IFN20230102）
