GNU Radio Implementation and Demonstration of MALIN: “Multi-Arm bandits Learning for Internet-of-things Networks”

Abstract

We implement1an IoT network the following way: one gateway, one or several intelligent (learning) objects, embedding the proposed solution, and a traffic generator that emulates radio interferences frommany other objects. Intelligent objects communicate with the gateway with a wireless ALOHA-based protocol with no specific overhead for learning needs. We model the network access as a discrete sequential decision making, and using theframework and algorithms from Multi-Armed Bandit (MAB) learning, we show that intelligent objects can improve their access to the network by using load complexity and decentralized algorithms, such as UCB and Thompson Sampling.