On channel selection schemes for spectrum sensing in cognitive radio networks

A MAC-layer channel selection scheme is proposed in this paper for efficient discovery of spectrum opportunities in the cognitive radio networks. In a practical uncertain cognitive environment, secondary users are unaware of the primary traffic statistics. Thus, in order to maximize the utilization, they need to obtain fairly accurate estimations of spectrum availability along with the actual sensing procedure, so as to learn when to sense which frequency channel. However, it is important not to waste the time, energy and the potential opportunities to explore primary's behavior in all frequency bands. In this work we investigate a low-complexity method referred to as modified myopic scheme, inspired by myopic strategy, whose gain converges to the highest possible throughput shortly after learning primary's activity and finding the most likely available channels. We also show that the secondary performance is inversely proportional to the lowest activity level of primary among all channels, rather than its average level. Therefore, highly-congested channels do not reduce the cognitive user's gain. The effect of sensing errors is also studied. Simulation results indicate the accuracy of our statements as well as the effectiveness of our scheme compared to the other strategies.