Abstract-In this paper, we consider the problem of exploiting spectrum resources for a secondary user (SU) of a wireless communication network. We suggest that Upper Confidence Bound (UCB) algorithms could be useful to design decision making strategies for SUs to exploit intelligently the spectrum resources based on their past observations. The algorithms use an index that provides an optimistic estimation of the availability of the resources to the SU. The suggestion is supported by some experimental results carried out on a specific dynamic spectrum access (DSA) framework.

This article draws a general retrospective view on the first 10 years of cognitive radio (CR). More specifically, we explore in this article decision making and learning for CR from an equipment perspective. Thus, this article depicts the main decision making problems addressed by the community as general dynamic configuration adaptation (DCA) problems and discuss the suggested solution proposed in the literature to tackle them. Within this framework dynamic spectrum management is briefly introduced as a specific instantiation of DCA problems. We identified, in our analysis study, three dimensions of constrains: the environment's, the equipment's and the user's related constrains. Moreover, we define and use the notion of a priori knowledge, to show that the tackled challenges by the radio community during first 10 years of CR to solve decision making problems have often the same design space, however they differ by the a priori knowledge they assume available. Consequently, we suggest in this article, the "a priori knowledge" as a classification criteria to discriminate the main proposed techniques in the literature to solve configuration adaptation decision making problems. We finally discuss the impact of sensing errors on the decision making process as a prospective analysis.

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In this paper we consider the problem of exploiting spectrum resources within the Opportunistic Spectrum Access context. We mainly focus on the case where one secondary user (SU) probes a pool of possibly available channels dedicated to a primary network. The SU is assumed to have imperfect sensing abilities. We, first, model the problem as a Multi-Armed Bandit problem with sensing errors. Then, we suggest to analyze the performances of the well known Upper Confidence Bound algorithm 1 within this framework, and show that we still can obtain an order optimal channel selection behavior. Finally we compare these results to those obtained in the case of perfect sensing. Simulation results are provided to support the suggested approach.