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Multi-Armed Bandit Learning in IoT Networks: Learning Helps Even in Non-stationary Settings

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Cognitive Radio Oriented Wireless Networks (CrownCom 2017)

Abstract

Setting up the future Internet of Things (IoT) networks will require to support more and more communicating devices. We prove that intelligent devices in unlicensed bands can use Multi-Armed Bandit (MAB) learning algorithms to improve resource exploitation. We evaluate the performance of two classical MAB learning algorithms, \(\mathrm {UCB}_1\) and Thomson Sampling, to handle the decentralized decision-making of Spectrum Access, applied to IoT networks; as well as learning performance with a growing number of intelligent end-devices. We show that 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 in term of successful transmission probabilities, and has near optimal performance even in non-stationary and non-i.i.d. settings with a majority of intelligent devices.

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Notes

  1. 1.

    In the experiments below, p is about \(10^{-3}\), because in a crowded network p should be smaller than \(N_c / (S + D)\) for all devices to communicate successfully (in average).

  2. 2.

    This optimal policy needs an oracle seeing the entire system, and affecting all the dynamic devices, once and for all, in order to avoid any signaling overhead.

  3. 3.

    We tried similar experiments with other values for \(N_c\) and this repartition vector, and results were similar for non-homogeneous repartitions. Clearly, the problem is less interesting for homogeneous repartition, as all channels appear the same for dynamic devices, and so even with D small in comparison to S, the system behaves like in Fig. 2d, where the performance of the five approaches are very close.

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Acknowledgements

This work is supported by the French National Research Agency (ANR), under the projects SOGREEN (grant coded: N ANR-14-CE28-0025-02) and BADASS (N ANR-16-CE40-0002), by Région Bretagne, France, by the French Ministry of Higher Education and Research (MENESR) and ENS Paris-Saclay.

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Correspondence to Rémi Bonnefoi .

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Bonnefoi, R., Besson, L., Moy, C., Kaufmann, E., Palicot, J. (2018). Multi-Armed Bandit Learning in IoT Networks: Learning Helps Even in Non-stationary Settings. In: Marques, P., Radwan, A., Mumtaz, S., Noguet, D., Rodriguez, J., Gundlach, M. (eds) Cognitive Radio Oriented Wireless Networks. CrownCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 228. Springer, Cham. https://doi.org/10.1007/978-3-319-76207-4_15

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  • DOI: https://doi.org/10.1007/978-3-319-76207-4_15

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