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Intelligent Radar Signal Detection for Future Generation Wireless Networks Using Deep Learning | IEEE Conference Publication | IEEE Xplore

Intelligent Radar Signal Detection for Future Generation Wireless Networks Using Deep Learning


Abstract:

The radio frequency (RF) spectrum is a limited and important resource that is used for communications and sensing systems. However, as the RF technology becomes more comp...Show More

Abstract:

The radio frequency (RF) spectrum is a limited and important resource that is used for communications and sensing systems. However, as the RF technology becomes more compact, expensive, and widely used, it becomes increasingly difficult to limit radar interference and share the spectrum. This involves the development of adaptive RF detection and classification techniques for intelligent radar in order to avoid the interference in future generation wireless networks. In this paper, a well-known deep neural network model named VGG16 was utilized to detect radar signals in the radio spectrum, even when they overlapped with Long-Term Evolution (LTE) and Wireless Local Area Network (WLAN) signals. The proposed approach can be extended to the 5G/6G environment. In this paper, we used an open-access dataset that was acquired through the air using a USRP N210 RF front end, and we also employed the combined amplitude plus phase shift representation. A total of 37,780 images were split into training and testing at the ratio of 8:2, and both training and testing accuracy yielded positive outcomes. The simulation result verifies that this concept correctly recognized the radar signal while the signal was overlapped with other band signals.
Date of Conference: 19-21 October 2022
Date Added to IEEE Xplore: 25 November 2022
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Conference Location: Jeju Island, Korea, Republic of

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