Minimizing the Detection Error in Cooperative Spectrum Sensing using Teaching Learning Based Optimization(TLBO)

Keraliya Divyesh R, Ashalata Kulshrestha
<span title="2017-02-25">2017</span> <i title="ESRSA Publications Pvt. Ltd."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3j6n6lpsjndinobmibtprywohe" style="color: black;">International Journal of Engineering Research and</a> </i> &nbsp;
Cognitive radio (CR) is a new paradigm in wireless communication system which is use for efficient utilization of radio frequency (RF) spectrum or RF channel for future wireless communication. Cooperative spectrum sensing is a key technology in cognitive radio networks (CRNs) to detect spectrum holes by combining sensing result of multiple cognitive radio users. This sensing information from CR users combines at the Fusion center (common receiver) by soft combination or conventional hard
more &raquo; ... tion techniques. Sensing error minimization is an important aspect of cooperative spectrum sensing that needs attention. In this paper, the use of teaching learning based optimization (TLBO) under MINI-MAX criterion is proposed to optimize the weighting coefficients vector of energy level of spectrum sensing information so that the total probability of error is minimized. The TLBO algorithm investigates the best weighting coefficient vector which minimizes total probability of error. The performance of the TLBO based method is analysed and compared with conventional soft decision fusion schemes like EGC as well as hard decision fusion method like AND,OR, Majority etc. Simulation results show that the proposed scheme minimizes the detection error compared to conventional soft decision fusion schemes
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