In spectrum overlay cognitive wireless networks, when the secondary users are blind, i.e. have no prior knowledge of primary users' activities, a major question remains as to what strategy to adopt to learn the primary state information online while sensing the spectrum for exploiting idle bands. To maximize secondary network spectral utilization while minimizing interference and collision, requires searching for a balance between choosing empirically best channel while investigating other channels for potential opportunities. Moreover, competition should also be resolved to prevent congestion and collision among secondary users who tend to access the same idle channel. In this paper, by compressing the inessential exploration phase, we first propose an asymptotically optimal, fast-converging channel selection algorithm referred to as modified-myopic strategy for the single-user scenario based on the result of multi-armed bandit. Through analysis and simulation modeling, we evaluate the performance of our approach and compare it to that of other strategies in the literature. Next we adopt a game-theoretic model and extend our algorithm to design a fair and low-complexity access strategy for multi-user case based on a generalized CSMA/CA scheme. Finally, we show the improved efficiency of the proposed approach, in particular significantly enhanced for dense networks.
['Nazanin Rastegardoost', 'Bijan Jabbari']
Blind channel selection strategies for distributed cognitive MAC