68 research outputs found
On Myopic Sensing for Multi-Channel Opportunistic Access: Structure, Optimality, and Performance
We consider a multi-channel opportunistic communication system where the
states of these channels evolve as independent and statistically identical
Markov chains (the Gilbert-Elliot channel model). A user chooses one channel to
sense and access in each slot and collects a reward determined by the state of
the chosen channel. The problem is to design a sensing policy for channel
selection to maximize the average reward, which can be formulated as a
multi-arm restless bandit process. In this paper, we study the structure,
optimality, and performance of the myopic sensing policy. We show that the
myopic sensing policy has a simple robust structure that reduces channel
selection to a round-robin procedure and obviates the need for knowing the
channel transition probabilities. The optimality of this simple policy is
established for the two-channel case and conjectured for the general case based
on numerical results. The performance of the myopic sensing policy is analyzed,
which, based on the optimality of myopic sensing, characterizes the maximum
throughput of a multi-channel opportunistic communication system and its
scaling behavior with respect to the number of channels. These results apply to
cognitive radio networks, opportunistic transmission in fading environments,
and resource-constrained jamming and anti-jamming.Comment: To appear in IEEE Transactions on Wireless Communications. This is a
revised versio
Dynamic multichannel access with imperfect channel state detection
Abstract—A restless multi-armed bandit problem that arises in multichannel opportunistic communications is considered, where channels are modeled as independent and identical Gilbert–Elliot channels and channel state detection is subject to errors. A simple structure of the myopic policy is established under a certain condition on the false alarm probability of the channel state detector. It is shown that myopic actions can be obtained by maintaining a simple channel ordering without knowing the underlying Markovian model. The optimality of the myopic policy is proved for the case of two channels and conjectured for general cases. Lower and upper bounds on the performance of the myopic policy are obtained in closed-form, which characterize the scaling behavior of the achievable throughput of the multichannel opportunistic system. The approximation factor of the myopic policy is also analyzed to bound its worst-case performance loss with respect to the optimal performance. Index Terms—Cognitive radio, dynamic multichannel access, myopic policy, restless multi-armed bandit
On Optimality of Myopic Sensing Policy with Imperfect Sensing in Multi-channel Opportunistic Access
We consider the channel access problem under imperfect sensing of channel
state in a multi-channel opportunistic communication system, where the state of
each channel evolves as an independent and identically distributed Markov
process. The considered problem can be cast into a restless multi-armed bandit
(RMAB) problem that is of fundamental importance in decision theory. It is
well-known that solving the RMAB problem is PSPACE-hard, with the optimal
policy usually intractable due to the exponential computation complexity. A
natural alternative is to consider the easily implementable myopic policy that
maximizes the immediate reward but ignores the impact of the current strategy
on the future reward. In this paper, we perform an analytical study on the
optimality of the myopic policy under imperfect sensing for the considered RMAB
problem. Specifically, for a family of generic and practically important
utility functions, we establish the closed-form conditions under which the
myopic policy is guaranteed to be optimal even under imperfect sensing. Despite
our focus on the opportunistic channel access, the obtained results are generic
in nature and are widely applicable in a wide range of engineering domains.Comment: 21 pages regular pape
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