Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning
This work addresses communication reliability in VEC for vehicles, but it is incremental as it combines existing methods like MADDPG and BCD for a specific application.
The paper tackles the problem of communication interruptions in vehicular edge computing (VEC) due to obstacles by using reconfigurable intelligent surfaces (RIS) and proposes a control scheme for power allocation and phase-shift optimization. Simulation results show that their scheme outperforms centralized DDPG and random schemes.
Vehicular edge computing (VEC) is an emerging technology that enables vehicles to perform high-intensity tasks by executing tasks locally or offloading them to nearby edge devices. However, obstacles such as buildings may degrade the communications and incur communication interruptions, and thus the vehicle may not meet the requirement for task offloading. Reconfigurable intelligent surfaces (RIS) is introduced to support vehicle communication and provide an alternative communication path. The system performance can be improved by flexibly adjusting the phase-shift of the RIS. For RIS-assisted VEC system where tasks arrive randomly, we design a control scheme that considers offloading power, local power allocation and phase-shift optimization. To solve this non-convex problem, we propose a new deep reinforcement learning (DRL) framework that employs modified multi-agent deep deterministic policy gradient (MADDPG) approach to optimize the power allocation for vehicle users (VUs) and block coordinate descent (BCD) algorithm to optimize the phase-shift of the RIS. Simulation results show that our proposed scheme outperforms the centralized deep deterministic policy gradient (DDPG) scheme and random scheme.