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The IEEE 802.22 is a new cognitive radio standard that is aimed at extendingwireless outreach to rural areas. Known as wireless regional area networks, anddesigned based on the not-to-interfere spectrum sharing model, WRANs arechannelized and centrally-controlled networks working on the under-utilizedUHF/VHF TV bands to establish communication with remote users, so-calledcustomer premises equipment (CPEs). Despite the importance of reliable andinterference-free operation in these frequencies, spectrum sensing fusionmechanisms suggested in IEEE 802.22 are rudimentary and fail to satisfy thestringent mandated sensing requirements. Other deep-rooted shortcomings areperformance non-uniformity over different signal-to-noise-ratio regimes,unbalanced performance, instability and lack of flexibility. Inspired by theseobservations, in this paper we propose a distributed spectrum sensing techniquefor WRANs, named multi-channel learning-based distributed sensing fusionmechanism (MC-LDS). MC-LDS is demonstrated to be self-trained, stable and tocompensate for fault reports through its inherent reward-penalty approach.Moreover, MC-LDS exhibits a better uniform performance in all traffic regimes,is fair (reduces the false-alarm/misdetection gap), adjustable (works withseveral degrees of freedom) and bandwidth efficient (opens transmissionopportunities for more CPEs). Simulation results and comparisons unanimouslycorroborate that MC-LDS outperforms IEEE 802.22 recommended algorithms, i.e.,the AND, OR and VOTING rules.

A Multi-Channel Spectrum Sensing Fusion Mechanism for Cognitive Radio Networks: Design and Application to IEEE 802.22 WRANs