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Deep learning based detection for communications systems with radar interference
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Liu, Chenguang, Chen, Yunfei and Yang, Shuang-Hua (2022) Deep learning based detection for communications systems with radar interference. IEEE Transactions on Vehicular Technology, 71 (6). pp. 6245-6254. doi:10.1109/TVT.2022.3158692 ISSN 0018-9545.
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Official URL: https://doi.org/10.1109/TVT.2022.3158692
Abstract
Due to the increasing demand for spectrum resources, the co-existence of communications and radar systems has been proposed that allows radar and communications systems to operate in the same frequency band. On the other hand, deep learning has shown great potential in revolutionizing communications systems. In this work, we investigate the use of deep learning in communications systems subject to interference from radar systems. Specifically, we consider a single-carrier communications system. Linear frequency-modulated (LFM) and frequency-modulated continuous-wave (FMCW) are considered for radar. Several important system parameters, including the level of noise and interference, the radar interference coverage, the symbol duration, feature extraction methods and the number of hidden layers are investigated for the performance of the detector. Fully connected deep neural network (FCDNN) and long short-term memory (LSTM) detectors are implemented, where principle component analysis (PCA) is applied to preprocess the observed signals for the FCDNN detector. Numerical results show that the learning-based detector achieves comparable performance in the radar-communication system to the traditional detector but without interference cancellation. Preprocessing the received signals with PCA can improve the performance of FCDNN when interference is strong. Also, LSTM shows more robust performance than FCDNN when the channel has time-related distortion.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Wireless communication systems , Deep learning (Machine learning) , Radar -- Interference , Signal detection , Neural computers | |||||||||||||||
| Journal or Publication Title: | IEEE Transactions on Vehicular Technology | |||||||||||||||
| Publisher: | IEEE | |||||||||||||||
| ISSN: | 0018-9545 | |||||||||||||||
| Official Date: | June 2022 | |||||||||||||||
| Dates: |
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| Volume: | 71 | |||||||||||||||
| Number: | 6 | |||||||||||||||
| Page Range: | pp. 6245-6254 | |||||||||||||||
| DOI: | 10.1109/TVT.2022.3158692 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
| Date of first compliant deposit: | 9 March 2022 | |||||||||||||||
| Date of first compliant Open Access: | 10 March 2022 | |||||||||||||||
| RIOXX Funder/Project Grant: |
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