Abstract
Recently, the utilization of Radio Frequency (RF) devices has increased exponentially over numerous vertical platforms. This rise has led to an abundance of Radio Frequency Interference (RFI) continues to plague RF systems today. The continued crowding of the RF spectrum makes RFI efficient and lightweight mitigation critical. Detecting and localizing the interfering signals is the foremost step for mitigating RFI concerns. Addressing these challenges, we propose a novel and lightweight approach, namely RaFID, to detect and locate the RFI by incorporating deep neural networks (DNNs) and statistical analysis via batch-wise mean aggregation and standard deviation (SD) calculations. RaFID investigates the generation of an expected signal using DNNs within the time domain. We performed the statistical analysis to compare our generated expected signal with the received signal to detect the existence of interference and determine interference frequency. Experimental results show that signal estimation is accurate, with a mean squared error of 0.012 and an average run-time of 0.5 seconds.
Recommended Citation
L. A. Smith et al., "RaFID: A Lightweight Approach To Radio Frequency Interference Detection In Time Domain Using LSTM And Statistical Analysis," Proceedings - IEEE International Conference on Mobile Data Management, pp. 209 - 214, Institute of Electrical and Electronics Engineers, Jan 2023.
The definitive version is available at https://doi.org/10.1109/MDM58254.2023.00043
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Deep Learning; LSTM; Radio Frequency Interference; Time Domain
International Standard Serial Number (ISSN)
1551-6245
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2023
