UCB Algorithm for Exponential Distributions
This addresses spectrum sensing and allocation in cognitive networks, but it is incremental as it adapts an existing method to a specific distribution.
The paper tackles the Multi-Armed Bandit problem for exponentially distributed rewards, common in Rayleigh fading channels, by introducing the MUCB algorithm, which achieves order-optimal performance with low complexity.
We introduce in this paper a new algorithm for Multi-Armed Bandit (MAB) problems. A machine learning paradigm popular within Cognitive Network related topics (e.g., Spectrum Sensing and Allocation). We focus on the case where the rewards are exponentially distributed, which is common when dealing with Rayleigh fading channels. This strategy, named Multiplicative Upper Confidence Bound (MUCB), associates a utility index to every available arm, and then selects the arm with the highest index. For every arm, the associated index is equal to the product of a multiplicative factor by the sample mean of the rewards collected by this arm. We show that the MUCB policy has a low complexity and is order optimal.