ChinaCom2008-Wireless Communications and Networking Symposium

Research Article

Improving Spectrum Sensing by Counting Rules for Cognitive Radio

  • @INPROCEEDINGS{10.1109/CHINACOM.2008.4685016,
        author={wenzhong wang and Weixia ZOU and Zheng ZHOU and Honggang ZHANG and Yabin Ye},
        title={Improving Spectrum Sensing by Counting Rules for Cognitive Radio},
        proceedings={ChinaCom2008-Wireless Communications and Networking Symposium},
        publisher={IEEE},
        proceedings_a={CHINACOM2008-WCN},
        year={2008},
        month={11},
        keywords={Cognitive Radio (CR); Spectrum Sensing; Local Decision Fusion; Energy Detector; Matched Filter},
        doi={10.1109/CHINACOM.2008.4685016}
    }
    
  • wenzhong wang
    Weixia ZOU
    Zheng ZHOU
    Honggang ZHANG
    Yabin Ye
    Year: 2008
    Improving Spectrum Sensing by Counting Rules for Cognitive Radio
    CHINACOM2008-WCN
    IEEE
    DOI: 10.1109/CHINACOM.2008.4685016
wenzhong wang1,*, Weixia ZOU1, Zheng ZHOU1, Honggang ZHANG2, Yabin Ye3
  • 1: Key Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Post and Telecommunication,Beijing 100876
  • 2: Zhejiang University,Beijing 100876
  • 3: Create-Net, Italy
*Contact email: wang_wenzhong@126.com

Abstract

Spectrum sensing is crucial for the success of cognitive radio because it will guarantee service improvement to unlicensed users and avoidance of interference to licensed users. However this new radio functionality will place severe sensitivity requirement on individual radio. Local decision fusion as a low complexity method can improve the detection performance by simply increasing the number of times of observation and decision. This paper analyses fusion rules of local decision and deduces their properties and conditions under the assumption that all the decisions are independent and follow the same probability distributions. These properties and conditions can be used to increase the detection probability and to lower the false alarm probability. Finally computation examples show that the performance of local detector can be optimal when choosing appropriate decision number and fusion rule.