Cooperative user association and resource allocation for task offloading in hybrid GEO‐LEO satellite networks. (2nd November 2021)

• Record Type: Journal Article
• Authors: Leng, Tao; Duan, Pengfei; Hu, Dongwei; Cui, Gaofeng; Wang, Weidong
• Is Part Of: International journal of satellite communications and networking. Volume 40:Number 3(2022)
• Page Start: 230
• Page End: 243
• Publication Date: 2021-11-02
• Subjects: GEO -- LEO -- resource allocation -- task offloading -- user association; Artificial satellites in telecommunication -- Periodicals; Digital communications -- Periodicals; 621.3825
• DOI: 10.1002/sat.1436
• Languages: English
• ISSNs: 1542-0973

Abstract

Summary: Hybrid geosynchronous earth orbit (GEO) and low earth orbit (LEO) satellite networks play an important role in future satellite‐assisted internet of things (S‐IoT). However, the limited satellite on‐board communication and computing resource poses a large challenge for the task offloading in the hybrid GEO‐LEO satellite networks. In this paper, the problem of task offloading is formulated as a cooperative user association and resource allocation problem. To tackle the problem, we model it as a Markov decision process and decompose it into two sub‐problems, which are sequential decisions for user association and resource allocation with fixed user association conditions. Deep reinforcement learning (DRL) is adopted to make sequential decisions to achieve long‐term benefits, and convex optimization method is utilized to obtain optimal communication and computing resource allocation. Simulation results show that the proposed method is superior to other referred schemes. Abstract : A framework for task offloading in hybrid geosynchronous earth orbit (GEO) and low earth orbit (LEO) satellite network is proposed. Meanwhile, deep reinforcement learning (DRL) and convex optimization method are both adopted to solve the complex problem formulated. Simulation results demonstrate the performance of the proposed method.