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We develop algorithms that find and track the optimal solution trajectory of time-varying convex optimization problems that consist of local and network-related objectives. The algorithms are derived from the prediction-correction methodology, which corresponds to a strategy where the time-varying problem is sampled at discrete time instances, and then, a sequence is generated via alternatively executing...
In this paper we consider decentralized multi-user online learning of unused spectrum bands as an opportunistic spectrum access (OSA) problem. There is a set of M secondary users exploiting the spectrum opportunities in K channels. We develop a distributed algorithm for the secondary users that will learn the optimal allocation with logarithmic regret. Thus, our algorithm achieves the fastest convergence...
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