A Game Theoretic Approach for D2D Assisted Uncoded Caching in IoT Networks

Jiajie Ren () and Chang Guo
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Jiajie Ren: School of Artificial Intelligence and Information Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Chang Guo: College of Information, Mechanical, and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China

Future Internet, 2025, vol. 17, issue 9, 1-24

Abstract: Content caching and exchange through device-to-device (D2D) communications can offload data from the centralized base station and improve the quality of users’ experience. However, existing studies often overlook the selfish nature of user equipment (UE) and the heterogeneity of content preferences, which limits their practical applicability. In this paper, we propose a novel incentive-driven uncoded caching framework modeled as a Stackelberg game between a base station (BS) and cache-enabled UEs. The BS acts as the leader by determining the unit incentive reward, while UEs jointly optimize their caching strategies as followers. The particular challenge in our formulation is that the uncoded caching decisions make the UEs’ total utility maximization problem into a non-convex integer programming problem. To address this, we map the UEs’ total utility maximization problem into a potential sub-game and design a potential game-based distributed caching (PGDC) algorithm that guarantees convergence to the optimal joint caching strategy. Building on this, we further develop a dynamic iterative algorithm to derive the Stackelberg equilibrium by jointly optimizing the BS’s cost and the total utility of UEs. The simulation results confirm the existence of the Stackelberg Equilibrium and demonstrate that the proposed PGDC algorithm significantly outperforms benchmark caching schemes.

Keywords: D2D assisted IoT caching networks; incentive mechanism; Stackelberg game; potential game (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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