  The IEEE 802.22 is a new cognitive radio standard that is aimed at extending
wireless outreach to rural areas. Known as wireless regional area networks, and
designed based on the not-to-interfere spectrum sharing model, WRANs are
channelized and centrally-controlled networks working on the under-utilized
UHF/VHF TV bands to establish communication with remote users, so-called
customer premises equipment (CPEs). Despite the importance of reliable and
interference-free operation in these frequencies, spectrum sensing fusion
mechanisms suggested in IEEE 802.22 are rudimentary and fail to satisfy the
stringent mandated sensing requirements. Other deep-rooted shortcomings are
performance non-uniformity over different signal-to-noise-ratio regimes,
unbalanced performance, instability and lack of flexibility. Inspired by these
observations, in this paper we propose a distributed spectrum sensing technique
for WRANs, named multi-channel learning-based distributed sensing fusion
mechanism (MC-LDS). MC-LDS is demonstrated to be self-trained, stable and to
compensate 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 with
several degrees of freedom) and bandwidth efficient (opens transmission
opportunities for more CPEs). Simulation results and comparisons unanimously
corroborate that MC-LDS outperforms IEEE 802.22 recommended algorithms, i.e.,
the AND, OR and VOTING rules.
