We consider opportunistic communications over multiple channels where the state ("good" or "bad") of each channel evolves as independent and identically distributed Markov processes. A user, with limited sensing and access capability, chooses one channel to sense and subsequently access (based on the sensed channel state) in each time slot. A reward is obtained when the user senses and accesses a "good" channel. The objective is to design the optimal channel selection policy that maximizes the expected reward accrued over time. This problem can be generally formulated as a Partially Observable Markov Decision Process (POMDP) or a restless multi-armed bandit process, to which optimal solutions are often intractable. We show in this paper that the myopic policy, with a simple and robust structure, achieves optimality under certain conditions. This result finds applications in opportunistic communications in fading environment, cognitive radio networks for spectrum overlay, and resource-constrained jamming and anti-jamming. Index Terms-Opportunistic access, cognitive radio, POMDP, restless multi-armed bandit process, myopic policy.
For more than two channels, the optimality of the myopic policy was proved in REF under certain conditions on channel parameters.
Optimality of Myopic Sensing in Multi-Channel Opportunistic Access
2008 IEEE International Conference on Communications
2008 IEEE International Conference on Communications
Computer Science Mathematics