Opportunistic exploitation of bandwidth resources through reinforcement learning

The enormous success of wireless technology has recently led to an explosive demand for, and hence a shortage of, bandwidth resources. This expected shortage problem is reported to be primarily due to the inefficient, static nature of current spectrum allotment methods. As an initial step towards solving this shortage problem, FCC1 opens up for the so-called opportunistic spectrum access (OSA), which allows unlicensed users to exploit unused licensed spectrum, but in a manner that limits interference to licensed users. Fortunately, technological advances enabled cognitive radios, which are viewed as intelligent communication systems that can learn from their surrounding environment by themselves, and adapt their internal operating parameters in real-time also by themselves to improve spectrum efficiency. Cognitive radios have recently been recognized as the key enabling technology for realizing OSA. In this work, we propose a machine learning-based scheme that will exploit the cognitive radios' capabilities to enable effective OSA, thus improving the efficiency of spectrum utilization. Our proposed learning technique does not require prior knowledge of the environment's characteristics and dynamics, yet can still achieve high performances by learning from interaction with the environment.