TY  - INPR
SN  - 2162-2337
PB  - Institute of Electrical and Electronics Engineers (IEEE)
UR  - https://doi.org/10.1109/lwc.2025.3542085
ID  - discovery10205325
N2  - This paper exploits the potential of edge intelligence empowered satellite-terrestrial networks, where users? computation tasks are offloaded to the satellites or terrestrial base stations. The computation task offloading in such networks involves the edge cloud selection and bandwidth allocations for the access and backhaul links, which aims to minimize the energy consumption under the delay and satellites? energy constraints. To address it, an alternating direction method of multipliers (ADMM)-inspired algorithm is proposed to decompose the joint optimization problem into small-scale subproblems. Moreover, we develop a hybrid quantum double deep Q-learning (DDQN) approach to optimize the edge cloud selection. This novel deep reinforcement learning architecture enables that classical and quantum neural networks process information in parallel. Simulation results confirm the efficiency of the proposed algorithm, and indicate that duality gap is tiny and a larger reward can be generated from a few data points compared to the classical DDQN.
KW  - Satellite-terrestrial networks
KW  -  edge intelligence
KW  - 
hybrid quantum computing
A1  - Huang, Siyue
A1  - Wang, Lifeng
A1  - Wang, Xin
A1  - Tan, Bo
A1  - Ni, Wei
A1  - Wong, Kai-Kit
JF  - IEEE Wireless Communications Letters
Y1  - 2025/02/14/
AV  - public
TI  - Edge Intelligence in Satellite-Terrestrial Networks with Hybrid Quantum Computing
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ER  -