Adaptive Spectrum Assessment for Opportunistic Access in Cognitive Radio Networks

Studies showed that the static nature of the traditional spectrum allocation methods, currently being used to share the radio spectrum, resulted in a plenty of unused spectrum opportunities that wireless devices can still potentially exploit. Fortunately, recent technological advances enabled software-defined radios (SDRs) that can switch from one spectrum band (SB) to another at minimum cost, thereby promoting dynamic and adaptive spectrum access and sharing. In this paper, we derive and study an adaptive spectrum assessment approach that allows devices to decide how to seek spectrum opportunities effectively. In the event when a decision is made in favor of discovering new opportunities, the proposed approach allows devices to determine the optimal number of SBs to be explored so that the device benefits from such an opportunistic spectrum access. This approach is optimal in that it strikes a balance between two conflicting needs: keeping spectrum assessment overhead low while increasing the likelihood of discovering spectrum opportunities. We study the effect of several network parameters, such as the primary traffic load, the secondary traffic load, and the collaboration level of the sensing method, on the optimal number of SBs that devices need to explore.