Dynamic Network Service Selection in Intelligent Reflecting Surface-Enabled Wireless Systems: Game Theory Approaches

TL;DR

This paper introduces game theory-based methods for dynamic service selection in IRS-enabled wireless networks, incorporating memory effects to improve user utilities and demonstrate system effectiveness.

Contribution

It proposes fractional evolutionary game models with memory effects for service selection, providing theoretical analysis and demonstrating improved utilities over traditional methods.

Findings

Memory effects lead to higher user utilities.

Unique equilibrium exists for both game models.

Numerical results confirm the effectiveness of the proposed approaches.

Abstract

In this paper, we address dynamic network selection problems of mobile users in an Intelligent Reflecting Surface (IRS)-enabled wireless network. In particular, the users dynamically select different Service Providers (SPs) and network services over time. The network services are composed of IRS resources and transmit power resources. To formulate the SP and network service selection, we adopt an evolutionary game in which the users are able to adapt their network selections depending on the utilities that they achieve. For this, the replicator dynamics is used to model the service selection adaptation of the users. To allow the users to take their past service experiences into account their decisions, we further adopt an enhanced version of the evolutionary game, namely fractional evolutionary game, to study the SP and network service selection. The fractional evolutionary game incorporates the memory effect that captures the users' memory on their decisions. We theoretically prove that both the game approaches have a unique equilibrium. Finally, we provide numerical results to demonstrate the effectiveness of our proposed game approaches. In particular, we have reveal some important finding, for instance, with the memory effect, the users can achieve the utility higher than that without the memory effect