A Game-Theoretic Perspective for Efficient Modern Random Access

TL;DR

This paper uses game theory to optimize user access policies in modern random access protocols, significantly improving throughput in IoT scenarios by finding Nash equilibria that balance rewards and costs.

Contribution

It introduces a game-theoretic framework for optimizing access strategies in modern random access mechanisms, including methods to achieve near-optimal throughput.

Findings

Mixed strategy with IRSA attains NE maximizing throughput for two users.

Method increases throughput by 30% over framed ALOHA in two-user case.

Three approaches to reach NE with up to 34% higher throughput than framed ALOHA.

Abstract

Modern random access mechanisms combine packet repetitions with multi-user detection mechanisms at the receiver to maximize the throughput and reliability in massive Internet of Things (IoT) scenarios. However, optimizing the access policy, which selects the number of repetitions, is a complicated problem, and failing to do so can lead to an inefficient use of resources and, potentially, to an increased congestion. In this paper, we follow a game-theoretic approach for optimizing the access policies of selfish users in modern random access mechanisms. Our goal is to find adequate values for the rewards given after a success to achieve a Nash equilibrium (NE) that optimizes the throughput of the system while considering the cost of transmission. Our results show that a mixed strategy, where repetitions are selected according to the irregular repetition slotted ALOHA (IRSA) protocol, attains a NE that maximizes the throughput in the special case with two users. In this scenario, our method increases the throughput by 30% when compared to framed ALOHA. Furthermore, we present three methods to attain a NE with near-optimal throughput for general modern random access scenarios, which exceed the throughput of framed ALOHA by up to 34%.