  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.
