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.


Electrical and Computer Engineering

Second Department

Computer Science

Keywords and Phrases

Deep Learning; LSTM; Radio Frequency Interference; Time Domain

International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version


File Type





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Publication Date

01 Jan 2023