KEYWORDS: Probability theory, Sensing systems, Optimization (mathematics), Energy efficiency, Cognitive modeling, Signal to noise ratio, Computer simulations, Signal detection, Target detection, Algorithms
In a periodic spectrum sensing framework where each frame consists of a sensing duration and a data transmitting
duration, the sensing duration to use is a trade-off between sensing performance and system efficiencies. The
relationships between sensing duration and state transition probability are analyzed firstly, when the licensed channel
stays in the idle and busy states respectively. Then a state transition probability based sensing duration optimization
algorithm is proposed, which can dynamically optimize the sensing duration of each frame. Analysis and simulation
results reveal that the proposed algorithm can use as little sensing duration in each frame as possible to satisfy the
sensing performance constraints so as to maximize the energy and transmitting efficiencies of the cognitive networks.
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