Two-Hop Cooperative Caching and UAVs Deployment Based on Potential Game

Author:

Bian Yuan1,Hu Jianbo1,Wang Shuo23ORCID,Hao Yukai4,Liu Wenjie1,Fu Chaoqi1

Affiliation:

1. School of Equipment Management and UAV Engineering, Air Force Engineering University, Xi’an 710043, China

2. State Key Laboratory of Astronautic Dynamics, Xi’an Satellite Control Center, Xi’an 710043, China

3. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China

4. Xi’an Institute of Aviation Computing Technology, Xi’an 710043, China

Abstract

This paper explores the joint cache placement and 3D deployment of Unmanned Aerial Vehicle (UAV) groups, utilizing potential game theory and a two-hop UAV cooperative caching mechanism, which could create a tradeoff between latency and coverage. The proposed scheme consists of three parts: first, the initial 2D location of UAV groups is determined through K-means, with the optimal altitude based on the UAV coverage radius. Second, to balance the transmission delay and coverage, the MOS (Mean Opinion Score) and coverage are designed to evaluate the performance of UAV-assisted networks. Then, the potential game is modeled, which transfers the optimization problem into the maximization of the whole network utility. The locally coupling effect resulting from action changes among UAVs is considered in the design of the potential game utility function. Moreover, a log-linear learning scheme is applied to solve the problem. Finally, the simulation results verify the superiority of the proposed scheme in terms of the achievable transmission delay and coverage performance compared with two other tested schemes. The coverage ratio is close to 100% when the UAV number is 25, and the user number is 150; in addition, this game outperforms the benchmarks when it comes to maximizing MOS of users.

Publisher

MDPI AG

Subject

Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering

Reference44 articles.

1. Green Resource Allocation based on Deep Reinforcement Learning in Content-Centric IoT;He;IEEE Trans. Emerg. Top. Comput.,2022

2. UAV Communications for 5G and Beyond: Recent Advances and Future Trends;Li;IEEE Internet Things J.,2019

3. Green UAV communications for 6G: A survey;Jiang;Chin. J. Aeronaut.,2022

4. UAV Caching in 6G Networks: A Survey on models, techniques, and applications;Trung;Phys. Commun.,2022

5. A Game-Theoretic Perspective on Resource Management for Large-Scale UAV Communication Networks;Chen;China Commun.,2021

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