Abstract
In this paper, we study the channel selection problem in edge computing-empowered cognitive machine-to-machine (CM2M) communications, where a massive number of machine type devices (MTDs) offload their computational tasks to a nearby edge server by opportunistically using the spectra that are temporarily unoccupied by primary users (PUs). We formulate the channel selection problem as an adversarial multi-armed bandit (MAB) problem, and combine the exponential-weight algorithm for exploration and exploitation (EXP3) and Lyapunov optimization to develop a learning-based energy-efficient solution named SEB-EXP3. It can find the long-term optimal channel selection strategy with guaranteed performance based on local information, while simultaneously achieving service reliability awareness, energy awareness, and data backlog awareness. Four heuristic algorithms are compared with SEB-EXP3 to demonstrate its effectiveness and reliability under various simulation settings.
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