Opportunistic bandwidth sharing through reinforcement learning

As an initial step toward solving the spectrum-shortage problem, the Federal Communications Commission (FCC) has started the so-called opportunistic spectrum access (OSA), which allows unlicensed users to exploit the unused licensed spectrum, but in a manner that limits interference to licensed users. Fortunately, technological advances have enabled cognitive radios, which have recently been recognized as the key enabling technology for realizing OSA. In this paper, 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 requires no prior knowledge of the environment's characteristics and dynamics, yet it can still achieve high performance by learning from interaction with the environment.