Dynamic Network Service Selection in IRS-Assisted Wireless Networks: A Game Theory Approach

TL;DR

This paper models the dynamic selection of network services in IRS-assisted wireless networks using evolutionary game theory, providing insights into user behavior and network optimization.

Contribution

It introduces an evolutionary game framework to analyze user choices in IRS-assisted networks, a novel approach for dynamic service selection modeling.

Findings

The game approach accurately predicts user behavior.

Simulation results validate the analytical equilibrium analysis.

The method improves understanding of resource allocation in IRS networks.

Abstract

In this letter, we investigate the dynamic network service provider (SP) and service selection in an intelligent reflecting surface (IRS)-assisted wireless network. In the network, mobile users select different network resources, i.e., transmit power and IRS resources, provided by different SPs. To analyze the SP and network service selection of the users, we formulate an evolutionary game. In the game, the users (players) adjust their selections of the SPs and services based on their utilities. We model the SP and service adaptation of the users by the replicator dynamics and analyze the equilibrium of the evolutionary game. Extensive simulations are provided to demonstrate consistency with the analytical results and the effectiveness of the proposed game approach.