Learning-Based Task Offloading and UAV Trajectory Optimization in SAGIN

Integrated space-air-ground network (SAGIN) has become a key approach to achieve seamless global coverage and efficient information transmission. However, the requirements for task latency and the limited energy supply of unmanned aerial vehicle UAV) pose significant challenges when it acts as edge nodes to provide computation offloading services. To address the computation offloading problem in SAGIN, this paper proposes an optimization solution based on the Deep Deterministic Policy Gradient (DDPG) algorithm. We introduce behavior noise and employ state normalization to preprocess the observed state. Our aim is to minimize the energy consumption and task processing latency of UAV by jointly optimizing the UAV's flight trajectory, transmission power, task offloading ratio, and offloading destinations selection. Experimental results validate the effectiveness and feasibility of the proposed method, showing that the DDPG algorithm has significant advantages over some other intelligent algorithms in reducing energy consumption and processing latency.

I. Introduction

In recent years, mobile data traffic has experienced exponential growth, driving the widespread development of terrestrial communication networks to meet the increasing communication demands. However, due to the substantial costs associated with deployment and maintenance, terrestrial mobile communication systems are ineffective in providing comprehensive coverage in certain remote areas (such as forests, oceans, and deserts) where deploying access points is challenging [1].