A Regret Minimization Approach to Frameless Irregular Repetition Slotted Aloha: IRSA-RM

Abstract

Wireless communications play an important part in the systems of the Internet of Things (IoT). Recently, there has been a trend towards long-range communications systems for IoT, including cellular networks. For many use cases, such as massive machine-type communications (mMTC), performance can be gained by going out of the classical model of connection establishment and adopting the random access methods. Associated with physical layer techniques such as Successive Interference Cancellation (SIC), or Non-Orthogonal Multiple Access (NOMA), the performance of random access can be dramatically improved, giving the novel random access protocol designs. This article studies one of these modern random access protocols: Irregular Repetition Slotted Aloha (IRSA). Because optimizing its parameters is not an easily solved problem, in this article, we use a reinforcement learning approach for that purpose. We adopt one specific variant of reinforcement learning, Regret Minimization, to learn the protocol parameters. We explain why it is selected, how to apply it to our problem with centralized learning, and finally, we provide both simulation results and insights into the learning process. The obtained results show the excellent performance of IRSA when it is optimized with Regret Minimization.