
Research Article
Deep Deterministic Policy Gradient Algorithm for Space/Aerial-Assisted Computation Offloading
@INPROCEEDINGS{10.1007/978-3-030-99200-2_39, author={Jielin Fu and Lei Liang and Yanlong Li and Junyi Wang}, title={Deep Deterministic Policy Gradient Algorithm for Space/Aerial-Assisted Computation Offloading}, proceedings={Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings}, proceedings_a={CHINACOM}, year={2022}, month={4}, keywords={Space-air-ground integrated network Edge computing Partial offloading Reinforcement learning}, doi={10.1007/978-3-030-99200-2_39} }
- Jielin Fu
Lei Liang
Yanlong Li
Junyi Wang
Year: 2022
Deep Deterministic Policy Gradient Algorithm for Space/Aerial-Assisted Computation Offloading
CHINACOM
Springer
DOI: 10.1007/978-3-030-99200-2_39
Abstract
Space-air-ground integrated network (SAGIN) has been envisioned as a promising architecture and computation offloading is a challenging issue, with the growing demand for computation-intensive applications in remote area. In this paper, we investigate a SAGIN edge computing architecture considering the energy consumption and delay of computation offloading, in which ground users can determine whether take advantage of edge server mounted on the unmanned aerial vehicle and satellite for partial offloading or not. Specifically, the optimization problem of minimizing the total cost is formulated as a Markov decision process, and we proposed a deep reinforcement learning-based method to derive the near-optimal policy, adopting the deep deterministic policy gradient (DDPG) algorithm to handle the large state space and continuous action space. Finally, simulation results demonstrate that the partial offloading scheme learned from proposed algorithm can substantially reduce the user devices’ total cost as compared to other greedy policies, and its performance is better than the binary offloading scheme learned from Deep Q-learning algorithm.