Distributed satellite information networks: Architecture, enabling technologies, and trends
arXiv:2412.12587 · doi:10.1007/s11432-024-4408-1
Abstract
Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, grant-free massive access, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.
References in corpus (26)
- Channel polarization: A method for constructing capacity-achieving codes for symmetric binary-input memoryless channels
- Satellite-to-ground quantum key distribution
- Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays
- Deep Joint Source-Channel Coding for Wireless Image Transmission
- Measurement device independent quantum key distribution over 404 km optical fibre
- Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends
- Threshold Saturation via Spatial Coupling: Why Convolutional LDPC Ensembles Perform so well over the BEC
- Massive MIMO Transmission for LEO Satellite Communications
- Sub-channel Assignment, Power Allocation and User Scheduling for Non-Orthogonal Multiple Access Networks
- A Survey on Non-Geostationary Satellite Systems: The Communication Perspective
- Spatially Coupled LDPC Codes Constructed from Protographs
- The Evolution of Quantum Secure Direct Communication: On the Road to the Qinternet
- Batched Sparse Codes
- Ground-Assisted Federated Learning in LEO Satellite Constellations
- Fast HARQ over Finite Blocklength Codes: A Technique for Low-Latency Reliable Communication
- Spatially Coupled Sparse Codes on Graphs - Theory and Practice
- Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G
- Channel Estimation for LEO Satellite Massive MIMO OFDM Communications
- Satellite Clustering for Non-Terrestrial Networks: Concept, Architectures, and Applications
- Low-Complexity Linear Diversity-Combining Detector for MIMO-OTFS
- Topology Virtualization and Dynamics Shielding Method for LEO Satellite Networks
- Location Management in IP-based Future LEO Satellite Networks: A Review
- Rate-Splitting Multiple Access and its Interplay with Intelligent Reflecting Surfaces
- Goal-Oriented Scheduling in Sensor Networks with Application Timing Awareness
- High Accuracy Distributed Kalman Filtering for Frequency and Phase Synchronization in Distributed Phased Arrays
- Digital Twin-Assisted Robust and Adaptive Resource Slicing in LEO Satellite Networks