Intelligent Reflecting Surface-Aided Spectrum Sensing for Cognitive Radio
| Autor: | Shaoe Lin, Beixiong Zheng, Fangjiong Chen, Rui Zhang |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Emerging Technologies (cs.ET) Control and Systems Engineering Computer Science - Information Theory Information Theory (cs.IT) FOS: Electrical engineering electronic engineering information engineering Computer Science - Emerging Technologies Electrical Engineering and Systems Science - Signal Processing Electrical and Electronic Engineering |
| Zdroj: | IEEE Wireless Communications Letters. 11:928-932 |
| ISSN: | 2162-2345 2162-2337 |
| DOI: | 10.1109/lwc.2022.3149834 |
| Popis: | Spectrum sensing is a key enabling technique for cognitive radio (CR), which provides essential information on the spectrum availability. However, due to severe wireless channel fading and path loss, the primary user (PU) signals received at the CR or secondary user (SU) can be practically too weak for reliable detection. To tackle this issue, we consider in this letter a new intelligent reflecting surface (IRS)-aided spectrum sensing scheme for CR, by exploiting the large aperture and passive beamforming gains of IRS to boost the PU signal strength received at the SU to facilitate its spectrum sensing. Specifically, by dynamically changing the IRS reflection over time according to a given codebook, its reflected signal power varies substantially at the SU, which is utilized for opportunistic signal detection. Furthermore, we propose a weighted energy detection method by combining the received signal power values over different IRS reflections, which significantly improves the detection performance. Simulation results validate the performance gain of the proposed IRS-aided spectrum sensing scheme, as compared to different benchmark schemes. Comment: Accepted by IEEE Wireless Communications Letters (5 pages, 4 figures) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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