Cooperative Spectrum Sensing with Deep Q-Network for Multimedia Applications
Authors: 
Qingying Wu, Benjamin K. Ng, Han Zhu, and Chan-Tong LamAuthors Info & Claims
Qingying Wu, Benjamin K. Ng, Han Zhu, and Chan-Tong LamAuthors Info & ClaimsOctober 2023
Pages 57 - 63
Abstract
With the gradually stricter requirement for multimedia applications, spectrum inefficiencies are urgent to be relieved by sensing and utilizing Spectrum Holes (SHs) over a wide spectrum. Cognitive Radio Sensor Network (CRSN) has drawn a lot of attention, which determines the state of Primary Users (PUs) by implementing Cooperative Spectrum Sensing (CSS), further overcoming various noise and fading issues in the radio environment. A survey on the application of Reinforcement Learning (RL) technology for CSS is conducted, especially through handling the performance optimization problem that cannot be achieved by traditional methods. Specifically, we transformed the traditional Fusion Center (FC) into an intelligent Agent that is responsible for making fusion decisions based on the results of Energy Detection (ED) technology. In this way, through learning from experience, the system performance in global probabilities can be improved by making fusion decisions as accurately as possible. Compared with traditional methods, comparison studies demonstrate the effectiveness of the proposed method in improving the CSS system performances, as well as its robustness in the face of various environments. The combination and complement of the traditional and the proposed scheme are also suggested in this paper.
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Index Terms
- Cooperative Spectrum Sensing with Deep Q-Network for Multimedia Applications
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Published: 29 October 2023
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