TY - JOUR
T1 - Joint Data Compression and Task Scheduling for LEO Satellite Networks
AU - He, Lijun
AU - Li, Shiyin
AU - Jia, Ziye
AU - Wang, Juncheng
AU - Han, Zhu
N1 - This work was supported in part by the National Natural Science Foundation of China under Grant 62201463 and 62301251, in part by the Natural Science Foundation of Jiangsu Province of China under Project BK20220883, and in part by NSF ECCS-2302469, CMMI-2222810, Toyota. Amazon and Japan Science and Technology Agency (JST) Adopting Sustainable Partnerships for Innovative Research Ecosystem (ASPIRE) JPMJAP2326.
Publisher Copyright:
© 2025 IEEE
PY - 2025/4/29
Y1 - 2025/4/29
N2 - We investigate a joint data compression and task scheduling problem for Low Earth Orbit Satellite Networks (LEOSNs), to maximize the sum weights of tasks while simultaneously minimizing the total data loss. First, we propose a novel Multi-Resource Conflict Graph (MRCG) model to characterize the intertwined communication and computation allocation conflicts inherent in typical data offloading processes of LEOSNs. Leveraging the proposed MRCG model, we then formulate the studied problem for LEOSNs as a linear integer program, to maximize the normalized weighted sum of both the sum task weight and the total data loss. We further explore the intrinsic structure of the linear integer program to propose an efficient solution using the Semi-Definite Relaxation (SDR) technique. Finally, the simulation results underscore that the synergistic optimization of data compression and task scheduling significantly facilitates the data offloading efficiency of LEOSNs, and the performances surpass existing benchmarks across a broad range of system parameters.
AB - We investigate a joint data compression and task scheduling problem for Low Earth Orbit Satellite Networks (LEOSNs), to maximize the sum weights of tasks while simultaneously minimizing the total data loss. First, we propose a novel Multi-Resource Conflict Graph (MRCG) model to characterize the intertwined communication and computation allocation conflicts inherent in typical data offloading processes of LEOSNs. Leveraging the proposed MRCG model, we then formulate the studied problem for LEOSNs as a linear integer program, to maximize the normalized weighted sum of both the sum task weight and the total data loss. We further explore the intrinsic structure of the linear integer program to propose an efficient solution using the Semi-Definite Relaxation (SDR) technique. Finally, the simulation results underscore that the synergistic optimization of data compression and task scheduling significantly facilitates the data offloading efficiency of LEOSNs, and the performances surpass existing benchmarks across a broad range of system parameters.
KW - Low earth orbit satellite networks
KW - data compression
KW - semi-definite relaxation
KW - task scheduling
UR - https://ieeexplore.ieee.org/document/10980043/
U2 - 10.1109/TVT.2025.3565571
DO - 10.1109/TVT.2025.3565571
M3 - Journal article
SN - 1939-9359
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
ER -
