Decentralized Power Allocation for MIMO-NOMA Vehicular Edge Computing Based on Deep Reinforcement Learning

TL;DR

This paper proposes a decentralized deep reinforcement learning approach using DDPG to optimize power allocation in MIMO-NOMA vehicular edge computing, effectively handling stochastic task arrivals and channel uncertainties.

Contribution

It introduces a decentralized DRL framework with DDPG for power allocation in VEC, addressing mobility-induced channel variations and stochastic task arrivals, which is novel compared to traditional centralized methods.

Findings

Outperforms existing power allocation schemes in simulations

Effectively manages stochastic task arrivals and channel uncertainties

Reduces power consumption and latency in VEC systems

Abstract

Vehicular edge computing (VEC) is envisioned as a promising approach to process the explosive computation tasks of vehicular user (VU). In the VEC system, each VU allocates power to process partial tasks through offloading and the remaining tasks through local execution. During the offloading, each VU adopts the multi-input multi-out and non-orthogonal multiple access (MIMO-NOMA) channel to improve the channel spectrum efficiency and capacity. However, the channel condition is uncertain due to the channel interference among VUs caused by the MIMO-NOMA channel and the time-varying path-loss caused by the mobility of each VU. In addition, the task arrival of each VU is stochastic in the real world. The stochastic task arrival and uncertain channel condition affect greatly on the power consumption and latency of tasks for each VU. It is critical to design an optimal power allocation scheme considering the stochastic task arrival and channel variation to optimize the long-term reward including the power consumption and latency in the MIMO-NOMA VEC. Different from the traditional centralized deep reinforcement learning (DRL)-based scheme, this paper constructs a decentralized DRL framework to formulate the power allocation optimization problem, where the local observations are selected as the state. The deep deterministic policy gradient (DDPG) algorithm is adopted to learn the optimal power allocation scheme based on the decentralized DRL framework. Simulation results demonstrate that our proposed power allocation scheme outperforms the existing schemes.