Satellite mobile edge computing (SMEC) enhanced satellite-terrestrial integrated networks (STIN) have attracted intensive attention to obtain seamless coverage and provide on-demand computation services. However, the cooperative task execution among low earth orbit (LEO) satellites is largely ignored in the SMEC-STIN. In this paper, we explore a hybrid cloud and edge computing architecture of the SMEC-STIN with coordinated task processing among neighboring LEO satellites. We investigate the computation offloading and resource allocation strategies to minimize the long-term cost in terms of a trade-off between task execution latency and energy consumption. We formulate the optimization problem as a Markov decision process and design a proximal policy optimization based deep reinforcement learning method to approximate the optimal solution with robust training stability and low storage demand. Simulation results validate the effectiveness of our proposed method.
Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Satellite-Terrestrial Integrated Networks
2022-06-01
848534 byte
Conference paper
Electronic Resource
English
Deep Reinforcement Learning Based Computation Offloading in UAV-Assisted Edge Computing
DOAJ | 2023
|