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.
Abstract
Satellite mega-constellations in Low Earth Orbit (LEO) are becoming an important part of next-generation non-terrestrial networks, but their operation remains challenging because of fast network topology variation, intermittent inter-satellite links, hardware disturbances, and strict Size, Weight, and Power (SWaP) constraints. Existing approaches based on Digital Twin (DT), Digital Twin Network (DTN), Software-Defined Networking (SDN), and Open Radio Access Network (O-RAN) provide useful building blocks for intelligent satellite networking, but they do not fully support real-time, predictive, and platform-aware network operation. In this paper, we propose a Digital Twin Satellite Network (DTSN) framework as a closed-loop architecture for reliable and intelligent management of LEO satellite constellations. The proposed 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. To validate the concept, we develop a constellation-scale cross-domain co-simulation using the NASA 42 spacecraft simulator and a Python-based DT bridge for a LEO constellation. The DT continuously ingests physical telemetry to manage a multi-domain threat environment, encompassing kinematic drift, hardware failures, and adversarial jamming over a 600-second flight window. By leveraging a predictive lookahead mechanism and an exponential sensor recovery model, the framework successfully isolates compromised nodes and triggers proactive network reconfiguration, thereby ensuring uninterrupted service and dynamic network resilience. These results show the potential of DTSN to support predictive and resilience-oriented satellite network operations.
ABSTRACT
The growing demand for universal internet access has highlighted the limitations of conventional terrestrial communication infrastructures, particularly in remote, rural, maritime, and disaster-affected regions. Satellite-Based Internet Communication Systems have emerged as a transformative solution capable of delivering broadband connectivity across vast geographical areas where traditional wired and wireless networks are either unavailable or economically infeasible. Recent advancements in Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geostationary Earth Orbit (GEO) satellite technologies have significantly improved communication speed, coverage, latency, and network reliability. Furthermore, the integration of Artificial Intelligence (AI), Software Defined Networking (SDN), and advanced signal processing techniques has enhanced satellite network performance and resource utilization. This paper presents a comprehensive study of Satellite-Based Internet Communication Systems and proposes an Artificial Intelligence-Enabled Satellite Communication Framework (AI-SCF) designed to optimize network efficiency, coverage, and service quality. The proposed framework integrates intelligent routing, adaptive bandwidth allocation, machine learning-based traffic prediction, and dynamic satellite resource management. Performance evaluation demonstrates significant improvements in throughput, latency reduction, coverage reliability, and network scalability compared with conventional satellite communication architectures. The findings indicate that satellite internet systems will serve as a fundamental pillar of future 6G communication ecosystems and global digital inclusion initiatives.
Keywords— Satellite Internet Communication, LEO Satellites, Broadband Connectivity, Artificial Intelligence, 6G Networks, Space Communication, Global Internet Access, Satellite Networking.
K. Venkatesh, Yadandla Anil, Thatla Venkatesh· International Scientific Jou...· 0 citations
This survey formally categorizes state-of-the-art DTN architectures into passive monitoring twins and active control twins, and provides an in-depth evaluation of their underlying enabling technologies, specifically ray-tracing, reconfigurable intelligent surfaces, artificial intelligence, and mobile edge computing.
Charalampos Oikonomidis, E. T. Michailidis, N. Miridakis· 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
Efficient long-term network evolution is becoming increasingly critical in dense 5G-Advanced and beyond cellular systems, where persistent traffic imbalances and localized congestion pose significant challenges that conventional short-term radio resource management alone cannot fully mitigate. This paper proposes a digital twin (DT)-enabled non-real-time (NRT) network evolution framework integrated with a large language model (LLM). Within this architecture, the digital twin provides a high-fidelity, controllable environment for evaluating infrastructure actions, while the LLM serves as a strategic orchestration engine that recommends cost-efficient network upgrades based on observed network states. Unlike traditional optimization methods that require exhaustive mathematical reformulations for each specific scenario, the proposed framework leverages the reasoning capabilities of LLMs to interpret operator objectives and constraints in natural language, generating structured evolution plans. The considered NRT action space encompasses antenna upgrades, bandwidth expansion, and new base station (BS) deployment. A techno-economic formulation is introduced to jointly evaluate load reduction performance and overall economic expenditure. Numerical results in a dense cellular scenario demonstrate that the framework effectively reduces peak resource utilization and provides diverse, coordinated evolution strategies tailored to varying network conditions.
Yukai Wang, Janghee Woo, G. Hahm et al.· International Conference on...· 0 citations
Satellite networks are emerging as core infrastructure for sixth-generation (6G) wireless systems, yet they face stringent constraints on bandwidth, onboard energy, processing capability, and link availability that bit-oriented communication cannot resolve. Semantic communication, which extracts and transmits task-relevant meaning rather than raw bits, offers a principled remedy across the satellite stack. This survey provides a layered overview of satellite semantic communication, covering: (i) the physical layer with channel-aware joint source-channel coding (JSCC) under long propagation delay, severe Doppler, and time-varying signal-to-noise ratio (SNR); (ii) onboard semantic processing with lightweight encoders, model compression, and in-orbit edge inference for radiation-hardened payloads; (iii) inter-satellite link (ISL) and network-layer routing, distributed federated learning across constellations, and 3rd Generation Partnership Project (3GPP) non-terrestrial network (NTN) integration; and (iv) representative applications spanning Earth observation (EO), satellite Internet of Things (IoT), direct-to-device (D2D), and deep-space scenarios. We synthesize quantitative gains across orbital regimes, identify open challenges in security, standardization, and heterogeneous-orbit interoperability, and outline directions including foundation-model compression, neuromorphic onboard computing, and carbon-aware orchestration for sustainable space connectivity.
Tung Son Do, Thanh Phung Truong, The-Vi Nguyen et al.· International Conference on...· 0 citations
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.
S. Shinde, Daniele Tarchi, Carlo Fischione· International Mediterranean...· 0 citations