Decentralized Spectrum Learning for IoT Wireless Networks Collision Mitigation

This paper describes the principles and implementation results of reinforcement learning algorithms on IoT devices for radio collision mitigation in ISM unlicensed bands. Learning is here used to improve both the IoT network capability to support a larger number of objects as well as the autonomy of IoT devices. We first illustrate the efficiency of the proposed approach in a proof-of-concept based on USRP software radio platforms operating on real radio signals. It shows how collisions with other RF signals present in the ISM band are diminished for a given IoT device. Then we describe the first implementation of learning algorithms on LoRa devices operating in a real LoRaWAN network, that we named IoTligent. The proposed solution adds neither processing overhead so that it can be ran in the IoT devices, nor network overhead so that no change is required to LoRaWAN. Real life experiments have been done in a realistic LoRa network and they show that IoTligent device battery life can be extended by a factor 2 in the scenarios we faced during our experiment

Proof-of-Concept System for Opportunistic Spectrum Access in Multi-user Decentralized Networks

International audiencePoor utilization of an electromagnetic spectrum and ever increasing demand for spectrum to support dataintensive services envisioned in 5G have led to surge of interests in paradigms such as cognitive radio,device-to-device communications, unlicensed LTE etc. Such paradigms can improve spectrum utilization viaopportunistic spectrum access (OSA) in which secondary (i.e., unlicensed) users (SUs) are allowed to transmitin the vacant licensed bands given that they do not cause any interference to the active licensed users. Over thelast decade, various spectrum detectors to check the status (i.e. vacant or occupied) of the frequency band havebeen studied and demonstrated but little attention has been paid to the task of frequency band selection fromwideband input signal. This is a challenging task especially in the decentralized network where SUs do notshare any information with each other. In this paper, a new decision making policy (DMP) has been proposedfor frequency band characterization and orthogonalization of SUs into optimal set of frequency bands. Inthe proposed DMP, Bayesian UCB (Bayes-UCB) algorithm is used for accurate characterization of frequencybands. Furthermore, Bayes-UCB based orthogonization scheme is proposed replacing existing randomizationbased schemes. Then, a testbed using USRP has been developed as a proof-of-concept system for analyzingthe performance of DMPs using real radio signals. Based on experimental results, we show that the proposedDMP is superior in terms of improvement in spectrum utilization when compared to existing DMPs. Addedadvantages of fewer number of frequency band switching as well as collisions make the proposed DMP energyefficient and hence, suitable for resource-constrained battery-operated radio terminals