Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Efficient Discovery of Spectrum Opportunities with MAC-Layer Sensing in Cognitive Radio Networks
IEEE Transactions on Mobile Computing
Optimal multiband joint detection for spectrum sensing in cognitive radio networks
IEEE Transactions on Signal Processing
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Exploiting location awareness toward improved wireless system design in cognitive radio
IEEE Communications Magazine
Speakeasy: the military software radio
IEEE Communications Magazine
What and how much to gain by spectrum agility?
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Spectrum Sharing for Multi-Hop Networking with Cognitive Radios
IEEE Journal on Selected Areas in Communications
Multi-Stage Pricing Game for Collusion-Resistant Dynamic Spectrum Allocation
IEEE Journal on Selected Areas in Communications
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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, FCC 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.