Contribution à l’apprentissage et à la prise de décision, dans des contextes d’incertitude, pour la radio intelligente

During the last century, most of the meaningful frequency bands were licensed to emerging wireless applications. Because of the static model of frequency allocation, the growing number of spectrum demanding services led to a spectrum scarcity. However, recently, series of measurements on the spectrum utilization showed that the different frequency bands were underutilized (sometimes even unoccupied) and thus that the scarcity of the spectrum resource is virtual and only due to the static allocation of the different bands to specific wireless services. Moreover, the underutilization of the spectrum resource varies on different scales in time and space offering many opportunities to an unlicensed user or network to access the spectrum. Cognitive Radio (CR) and Opportunistic Spectrum Access (OSA) were introduced as possible solutions to alleviate the spectrum scarcity issue.In this dissertation, we aim at enabling CR equipments to exploit autonomously communication opportunities found in their vicinity. For that purpose, we suggest decision making mechanisms designed and/or adapted to answer CR related problems in general, and more specifically, OSA related scenarios. Thus, we argue that OSA scenarios can be modeled as Multi-Armed Bandit (MAB) problems. As a matter of fact, within OSA contexts, CR equipments are assumed to have no prior knowledge on their environment. Acquiring the necessary information relies on a sequential interaction between the CR equipment and its environment. Finally, the CR equipment is modeled as a cognitive agent whose purpose is to learn while providing an improving service to its user. Thus, firstly we analyze the performance of UCB1 algorithm when dealing with OSA problems with imperfect sensing. More specifically, we show that UCB1 can efficiently cope with sensing errors. We prove its convergence to the optimal channel and quantify its loss of performance compared to the case with perfect sensing. Secondly, we combine UCB1 algorithm with collaborative and coordination mechanism to model a secondary network (i.e. several SUs). We show that within this complex scenario, a coordinated learning mechanism can lead to efficient secondary networks. These scenarios assume that a SU can efficiently detect incumbent users’ activity while having no prior knowledge on their characteristics. Usually, energy detection is suggested as a possible approach to handle such task. Unfortunately, energy detection in known to perform poorly when dealing with uncertainty. Consequently, we ventured in this Ph.D. to revisit the problem of energy detection limits under uncertainty. We present new results on its performances as well as its limits when the noise level is uncertain and the uncertainty is modeled by a log-normal distribution (as suggested by Alexander Sonnenschein and Philip M. Fishman in 1992). Within OSA contexts, we address a final problem where a sensor aims at quantifying the quality of a channel in fading environments. In such contexts, UCB1 algorithms seem to fail. Consequently, we designed a new algorithm called Multiplicative UCB (UCB) and prove its convergence. Moreover, we prove that MUCB algorithms are order optimal (i.e., the order of their learning rate is optimal). This last work provides a contribution that goes beyond CR and OSA. As a matter of fact, MUCB algorithms are introduced and solved within a general MAB framework.