Deep Reinforcement Learning based UAV-Assisted Maritime Network Computation Offloading Strategy

As maritime activities become more frequent, many computationally intensive applications have emerged. Unmanned aerial vehicles (UAVs) can be utilized to provide computing services for maritime networks. In this paper, a multi-UAV assisted mobile edge computing (MEC) network is designed. Each UAV is configured with a nano-server to provide computational offloading services for maritime users (MVs). The goal is to minimize the maximum processing delay of the multi-VAV assisted MEC network. We present an optimization problem for joint task offloading, resource allocation, and flight trajectory of UAVs. The above problem is an mixed non-integer linear programming problem (MINLP), which is transformed into a Markov decision process (MDP). A computational offload algorithm based on deep deterministic policy gradient (DDPG) is proposed to solve this problem. Simulation results reveal that the DDPG algorithm can achieve fast convergence and minimize processing delay compared with baseline algorithms.

I. Introduction

With the large-scale commercialization of 5G networks, the global industry has begun to explore the sixth generation of mobile communications (6G). Compared with 5G, 6G has higher bandwidth, coverage, and latency requirements. There will be many real-time applications in 6G networks. The user equipment (UE) is limited by computing resources and energy power and will struggle to handle these applications. Therefore, Mobile edge computing (MEC)[1] technology is applied to 6G to extend the computing power of UEs.