DeepSweep: Parallel and Scalable Spectrum Sensing via Convolutional Neural Networks
CoRR(2024)
摘要
Spectrum sensing is an essential component of modern wireless networks as it
offers a tool to characterize spectrum usage and better utilize it. Deep
Learning (DL) has become one of the most used techniques to perform spectrum
sensing as they are capable of delivering high accuracy and reliability.
However, current techniques suffer from ad-hoc implementations and high
complexity, which makes them unsuited for practical deployment on wireless
systems where flexibility and fast inference time are necessary to support
real-time spectrum sensing. In this paper, we introduce DeepSweep, a novel
DL-based transceiver design that allows scalable, accurate, and fast spectrum
sensing while maintaining a high level of customizability to adapt its design
to a broad range of application scenarios and use cases. DeepSweep is designed
to be seamlessly integrated with well-established transceiver designs and
leverages shallow convolutional neural network (CNN) to "sweep" the spectrum
and process captured IQ samples fast and reliably without interrupting ongoing
demodulation and decoding operations. DeepSweep reduces training and inference
times by more than 2 times and 10 times respectively, achieves up to 98 percent
accuracy in locating spectrum activity, and produces outputs in less than 1 ms,
thus showing that DeepSweep can be used for a broad range of spectrum sensing
applications and scenarios.
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