Deadline Scheduling in Energy Harvesting Networks: Competitive and Learning Algorithms

This paper considers the problem of maximizing the number of scheduled users that request to download data with deadlines from an energy-harvesting base station. This problem is solved based on two frameworks: online computation and online learning. In the first framework, an optimal offline and a deterministic competitive algorithms are designed. In the online learning framework, the multi-armed bandit approach is used to design a learning algorithm based on the well-known exponential-weight algorithm for exploration and exploitation. We bound its regret and show that it grows sub-linearly with time. Finally, we supplement our theoretical results by simulations to illustrate the performance of the proposed algorithms.