Enhancing Cognitive Radio Algorithms Through Efficient, Automatic Adaptation Management

In recent years, cognitive radios that follow dynamic spectrum access policies have been proposed to overcome spectrum scarcity and to make better use of spectrum opportunities while avoiding interference to other users. The central component of such a cognitive radio is the control algorithm driving its sensing, learning and adaptation process. These three tasks however are both computationally expensive and resource intensive and it is therefore in the cognitive radio´s best interest to minimize the time spent to sense, learn and adapt to its surroundings while still meeting its operational targets. In this paper, we present the rapid adaptation architecture, a statistical system that can be used in conjunction with existing cognitive radio control algorithms to speed up the learning and adaptation process without loosing significant accuracy. Through this system, control algorithms can be made more efficient by a factor of 2 or more, thus providing the cognitive radio with faster, more resource saving adaptations without major changes to the algorithm´s inner workings or the overall cognitive radio.