.png){kind=icon}
.png){kind=icon}
Return
Weighted cost minimization for LEO satellite and HAP cooperative edge computing network
.png){kind=icon}
PRE
AI
D
N
N
L
L
DOI：10.23919/jcc.fa.2025-0174.202604.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
6G is driving the evolution of Internet of Things from Internet of Everything to Intelligent Internet of Everything, requiring globally seamless communication coverage and efficient computing capabilities at network nodes. To this end, this paper proposes a low earth orbit (LEO) satellite and high altitude platform (HAP) cooperative edge computing network architecture, where ground users can either compute tasks locally or offload them to the HAP or access satellites. Particularly, when local computation resources are insufficient, access satellites can schedule available resources from other satellites via inter-satellite links. Specifically, access satellites incrementally expand a candidate set hop-by-hop until accumulated computation resources meet task requirements and then coordinate with satellites in the set. A weighted cost minimization problem of average energy consumption, average computation resource price, and task overflow rate is formulated under the latency constraint. Due to its mixed-integer nonlinearity, the problem is decomposed into three subproblems. An alternating iteration resource allocation algorithm (AIRAA) is designed to jointly optimize communication and computation resource allocation, while a heuristic algorithm is proposed for task offloading decision optimization. Simulation results demonstrate that the proposed algorithm exhibits good convergence and achieves weighted cost reductions of 45.6%, 18.4%, 33.7%, and 37.5% compared with four baseline schemes when the user-satellite link bandwidth is set to 150 MHz.
Keywords:
cooperative edge computing
high altitude platform
low earth orbit satellite
weighted cost
Journal
IF:
3.1
Papers:
1.8K
Citations:
5.0K
Organization
Cited Papers