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Setting up the future Internet of Things (IoT) networks will require tosupport more and more communicating devices. We prove that intelligent devicesin unlicensed bands can use Multi-Armed Bandit (MAB) learning algorithms toimprove resource exploitation. We evaluate the performance of two classical MABlearning algorithms, UCB1 and Thompson Sampling, to handle the decentralizeddecision-making of Spectrum Access, applied to IoT networks; as well aslearning performance with a growing number of intelligent end-devices. We showthat using learning algorithms does help to fit more devices in such networks,even when all end-devices are intelligent and are dynamically changing channel.In the studied scenario, stochastic MAB learning provides a up to 16% gain interm of successful transmission probabilities, and has near optimal performanceeven in non-stationary and non-i.i.d. settings with a majority of intelligentdevices.

Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings