{"title":"Simultaneous Transmit-Receive Processing for UAV Jammers: A Deep Neural Network Approach to Self-Interference Cancellation","authors":"Qianru Liu, Jiahao Zhang, Wei Li, Liang Zhou, Hengfeng Wang, Hao Wu, Jundi Wang","doi":"10.1049/ell2.70427","DOIUrl":null,"url":null,"abstract":"<p>To address the significant residual interference fluctuations caused by the dynamic coupling between hardware nonlinearities and time-varying channel characteristics in self-interference (SI) signals, this paper proposes a dual-layer SI cancellation (SIC) method based on convolutional long short-term memory deep neural networks (CLDNN). We establish a dual-layer cancellation model for full-duplex jammers and derive the interference cancellation expression under the combined effects of nonlinearity and time-varying channels. Furthermore, a CLDNN-based network incorporating high-order expansion terms is designed to break through the linear fitting limitations of traditional adaptive cancellation, thereby enhancing SIC performance. Simulation results confirm that the proposed dual-layer cancellation method significantly outperforms traditional least mean squares (LMS) algorithms, convolutional neural networks (CNN), and sampled-weight gated recurrent units (SW-GRU) methods, achieving a 26.37 dB improvement in interference cancellation ratio (ICR).</p>","PeriodicalId":11556,"journal":{"name":"Electronics Letters","volume":"61 1","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/ell2.70427","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronics Letters","FirstCategoryId":"5","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/ell2.70427","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
To address the significant residual interference fluctuations caused by the dynamic coupling between hardware nonlinearities and time-varying channel characteristics in self-interference (SI) signals, this paper proposes a dual-layer SI cancellation (SIC) method based on convolutional long short-term memory deep neural networks (CLDNN). We establish a dual-layer cancellation model for full-duplex jammers and derive the interference cancellation expression under the combined effects of nonlinearity and time-varying channels. Furthermore, a CLDNN-based network incorporating high-order expansion terms is designed to break through the linear fitting limitations of traditional adaptive cancellation, thereby enhancing SIC performance. Simulation results confirm that the proposed dual-layer cancellation method significantly outperforms traditional least mean squares (LMS) algorithms, convolutional neural networks (CNN), and sampled-weight gated recurrent units (SW-GRU) methods, achieving a 26.37 dB improvement in interference cancellation ratio (ICR).
期刊介绍:
Electronics Letters is an internationally renowned peer-reviewed rapid-communication journal that publishes short original research papers every two weeks. Its broad and interdisciplinary scope covers the latest developments in all electronic engineering related fields including communication, biomedical, optical and device technologies. Electronics Letters also provides further insight into some of the latest developments through special features and interviews.
Scope
As a journal at the forefront of its field, Electronics Letters publishes papers covering all themes of electronic and electrical engineering. The major themes of the journal are listed below.
Antennas and Propagation
Biomedical and Bioinspired Technologies, Signal Processing and Applications
Control Engineering
Electromagnetism: Theory, Materials and Devices
Electronic Circuits and Systems
Image, Video and Vision Processing and Applications
Information, Computing and Communications
Instrumentation and Measurement
Microwave Technology
Optical Communications
Photonics and Opto-Electronics
Power Electronics, Energy and Sustainability
Radar, Sonar and Navigation
Semiconductor Technology
Signal Processing
MIMO