Research Area: Machine Learning
Virtual reality (VR) services have become increasingly popular but presented challenges for wireless communications due to the large amounts of data requirements. In this work, we consider a dynamic changing VR scenario and propose a joint caching, computing, and communication (3 C) strategy, subject to bounded latency, power, caching, and computing constraints, to minimize long-term discounted delay and energy consumption for VR projection. Our approach involves a three-layer communication system consisting of a cloud server, UAV (Unmanned Aerial Vehicle) base stations with mMIMO (massive Multiple-Input Multiple-Output) acting as edge servers, and mobile user devices. To satisfy different users requirements, we design eight service routes for 3 C decisions. We then employ federated multi-agent deep reinforcement learning (RL) to help users obtain optimal service routes influenced by their location, orientation, and content preference, with edge servers acting as learning agents. For the RL part, we design multi-input and output actor and critic networks deployed on edge servers. For the Federated Learning (FL) part, we present the federated average process and mathematically prove its convergence. Simulation results demonstrate our proposed algorithm can effectively reduce training loss, converge smoothly, and significantly reduce both delay and energy consumption by approximately 17.2% and 23.5%, respectively.
Keywords:
Servers
Delays
Three-dimensional displays
Energy consumption
Edge computing
Task analysis
Base stations
Author(s) Name: Zhikai Liu,Navneet Garg,Tharmalingam Ratnarajah
Journal name: IEEE Transactions on Network Science and Engineering
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/TNSE.2023.3292570
Volume Information:
