Jul 2026· International Mediterranean Conference on Communications and Networking· pp. 1-6· 0 citations· 13 references
Abstract
The Open Radio Access Network (O-RAN) paradigm, with its open interfaces and intelligent functions, is a key enabler for next-generation wireless systems. We investigate the deployment of O-RAN-based network slice functions over Low Earth Orbit (LEO) satellite networks with Mobile Edge Computing (MEC) capabilities. To provide energy-efficient and low-latency services through distributed data processing, we formulate a slice function data offloading problem aimed at jointly optimizing end-to-end (E2E) latency and energy consumption. We model the problem as an MDP and propose a Deep Reinforcement Learning (DRL)-based solution. The proposed DRL agent learns efficient offloading policies by balancing computation and communication costs in the dynamic satellite environment. Simulation results show that our DRL-based approach significantly outperforms conventional benchmarks, achieving enhanced latency and energy performance, enabling intelligent orchestration of O-RAN slices over LEO satellite networks.
Integrated Terrestrial–Non-Terrestrial Networks (ITNTN), which combine terrestrial base stations (BSs), High-Altitude Platform Stations (HAPS), and Low-Earth Orbit (LEO) satellites, are key enablers of 6G communication and edge computing (EC) services. However, energy-limited BSs, particularly HAPS and satellites, pose significant sustainability challenges under continuous operation. To address this issue, we propose an on-demand EC server activation framework integrated with intelligent task offloading across ITNTN. A joint optimization problem is formulated to maximize task offloading success while satisfying energy and quality-of-service requirements. To solve it, we propose an online Q-learning policy that adaptively manages task offloading and EC server activation without prior knowledge of traffic dynamics. Simulation results show that the proposed method achieves superior task offloading success and energy efficiency compared to online heuristic and offline metaheuristic baselines. These findings highlight the importance of energyaware On-Off EC control for sustainable ITNTN systems.
Insaf Rzig, W. Jaafar, Safwan Alfattani· International Conference on...· 0 citations
This article outlines the fundamental principles of the dual-layer OTA model and introduces the adaptive BH mechanism designed for time-varying topologies, aiming to provide insights for the evolution of ubiquitous non-terrestrial intelligence.
Zhendong Li, Shaojie Wang, Zhou Su et al.· 0 citations
Satellite edge computing (SEC) has emerged as a promising paradigm to enhance in-orbit data processing capabilities and reduce transmission latency. However, satellite image processing tasks in SEC environments face critical challenges in efficient data handling, resource coordination, and transmission scheduling. The dynamic network topology and time-varying resource availability in satellite constellations further degrade the quality and stability of SEC services. To address these challenges, we propose a deep learning-based Collaborative Image Feature-extraction Task Optimization (CIFTO) framework. CIFTO dynamically distributes image processing workloads across multiple Low Earth Orbit (LEO) satellites, enabling continuous temporal updates for task allocation while significantly accelerating convergence and reducing computational overhead. By integrating temporal modeling and iterative optimization, CIFTO effectively mitigates the NP-hard nature of satellite task allocation. Furthermore, a lightweight satellite image processing model is designed to meet the strict constraints of on-orbit computation, achieving efficient image inference with minimal parameters. Extensive experimental evaluations demonstrate that the proposed framework ensures timely task completion, substantially lowers system-wide energy consumption, and enhances the adaptability and training efficiency of SEC services.
Xiaoteng Yang, Jie Feng, Lei Liu et al.· IEEE transactions on compute...· 0 citations
With the rapid development of artificial intelligence and low-earth orbit (LEO) satellite edge computing technology, there has been a rapid increase in the demand for intelligence networks and services among users in remote areas, such as federated learning (FL). We propose a satellite aided computation FL (SACFL) system in LEO ubiquitous edge computing (UEC) networks, aiming at improving the efficiency of FL tasks in remote areas. In the considered deployment scenario, the following key factors are considered: 1) terrestrial users in remote areas; 2) offloading data to satellites for aided computation; and 3) global aggregation on satellite. However, the highly dynamic characteristics and the uneven distribution of satellite computation resources pose significant challenges to the low-delay requirements of satellite-based FL tasks. To this end, we formulate an optimization problem to minimize delay by jointly considering access selection, computation offloading, aggregation satellite (AgS) selection, and computation resource allocation. To solve the formulated problem, we propose a novel multi-agent alternating (M2A) optimization method. Specifically, three independent agents are trained alternately to make decisions on access selection, computation offloading, and AgS selection. Comprehensive simulations demonstrate that the proposed method outperforms other benchmark algorithms in terms of convergence, delay, and FL accuracy.
Junyi Yang, Yafeng Ma, Zhenyu Xiao et al.· IEEE Transactions on Cogniti...· 0 citations
Numerical results indicate that the proposed framework outperforms benchmark schemes while accounting for traffic demands and EE, resulting in a mixed-integer nonlinear program (MINLP) for which finding a globally optimal solution is generally intractable.
Wooseok Cha, Kyeongsoo Kim, Seonghoon Kim et al.· IEEE Transactions on Wireles...· 0 citations
The proposed Digital Twin Satellite Network (DTSN) framework connects the physical satellite network with a synchronized virtual twin and combines real-time telemetry, Integrated Sensing and Communication (ISAC), predictive intelligence, and resilience-oriented control and successfully isolates compromised nodes and triggers proactive network reconfiguration.