We consider opportunistic communication over multiple channels where the state ("good" or "bad") of each channel evolves as independent and identically distributed Markov processes. A user, with limited channel 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 whenever the user senses and accesses a "good" channel. The objective is to design an optimal channel selection policy that maximizes the expected total (discounted or average) reward accrued over a finite or infinite horizon. This problem can be cast 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 a myopic policy that maximizes the immediate one-step reward is always optimal when the state transitions are positively correlated over time. When the state transitions are negatively correlated, we show that the same policy is optimal when the number of channels is limited to 2 or 3, while presenting a counterexample for the case of 4 channels. This result finds applications in opportunistic transmission scheduling in a fading environment, cognitive radio networks for spectrum overlay, and resource-constrained jamming and anti-jamming.Preliminary version of this work was presented at

Abstract-This paper considers the following stochastic control problem that arises in opportunistic spectrum access: a system consists of n channels where the state ("good" or "bad") of each channel evolves as independent and identically distributed Markov processes. A user can select exactly k channels to sense and access (based on the sensing result) in each time slot. A reward is obtained whenever the user senses and accesses a "good" channel. The objective is to design a channel selection policy that maximizes the expected discounted total reward accrued over a finite or infinite horizon. In our previous work we established the optimality of a greedy policy for the special case of k = 1 (i.e., single channel access) under the condition that the channel state transitions are positively correlated over time. In this paper we show under the same condition the greedy policy is optimal for the general case of k ≥ 1; the methodology introduced here is thus more general. This problem may be viewed as a special case of the restless bandit problem, with multiple plays. We discuss connections between the current problem and existing literature on this class of problems.

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In this paper we consider an extension to the classical definition of congestion games (CG) in which multiple users share the same set of resources and their payoff for using any resource is a function of the total number of users sharing it. The classical congestion games enjoy some very appealing properties, including the existence of a Nash equilibrium and that every improvement path is finite and leads to such a NE (also called the finite improvement property or FIP), which is also a local optimum to a potential function. On the other hand, this class of games does not model well the congestion or resource sharing in a wireless context, a prominent feature of which is spatial reuse. What this translates to in the context of a congestion game is that a user's payoff for using a resource (interpreted as a channel) is a function of the its number of its interfering users sharing that channel, rather than the total number among all users. This makes the problem quite different. We will call this the congestion game with resource reuse (CG-RR). In this paper we study intrinsic properties of such a game; in particular, we seek to address under what conditions on the underlying network this game possesses the FIP or NE. We also discuss the implications of these results when applied to wireless spectrum sharing.

Abstract-In this correspondence, we study the capacity region of a general wireless network by deriving fundamental upper bounds on a class of linear functionals of the rate tuples at which joint reliable communication can take place. The widely studied transport capacity is a specific linear functional: the coefficient of the rate between a pair of nodes is equal to the Euclidean distance between them. The upper bound on the linear functionals of the capacity region is used to derive upper bounds to scaling laws for generalized transport capacity: the coefficient of the rate between a pair of nodes is equal to some arbitrary function of the Euclidean distance between them, for a class of minimum distance networks. This upper bound to the scaling law meets that achievable by multihop communication over these networks for a wide class of channel conditions; this shows the optimality, in the scaling-law sense, of multihop communication when studying generalized transport capacity of wireless networks.