# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 6 Conclusions  
This paper has proposed a novel framework to integrate satellite operations and network management within NTNs. Achieving effective integration across these traditionally separate operational domains is critical to realizing the ambitious goals of future global networks, such as 6G. The framework developed, the CMS, facilitates this integration by providing a unified, task-driven approach to managing the operational demands of satellite constellations and terrestrial networks. The CMS is designed as a flexible task-scheduling system that centralizes operational control. It uses optimization scheduling algorithms to allocate resources effectively while accommodating satellite-specific constraints, network service requirements, and QoS metrics. The modular design of the CMS supports extensions for SDN-enabled systems and future automated MANO integration. This framework proposes three distinct novel architectures for SatOps-NetOps interaction, each suited to different levels of operational integration and network scenarios. These CMS-based architectures address key challenges in satellite and network interoperability, infrastructure management, operational dynamics, and technology deployment, providing tailored solutions for various network requirements and enhancing the overall management of integrated satellite and network operations. The practical deployment and widespread adoption of the CMS framework requires consideration of standardization pathways that can facilitate industry-wide implementation. Several routes present viable opportunities for CMS integration into existing operational frameworks. The 3 GPP represents the most direct pathway, given its ongoing NTN integration work through Releases 17-19, where the CMS task-scheduling approach could be incorporated as a normative framework for satellite operations management in future releases. The CCSDS offers another pathway for the satellite operations aspects, particularly given its established expertise in satellite mission planning standards and the CMS framework's extension of traditional CCSDS methodologies to incorporate network operations criteria. The initial scalability analysis presented in this work demonstrates computational performance characteristics up to 144 satellites. However, this analysis represents a limited scope compared to the mega-constellation scenarios envisioned for future 6G networks. Future work must address a broader scalability validation through large-scale testing, distributed CMS architectures, and optimization algorithm improvements to maintain acceptable performance for mega-constellation operations. Additionally, detailed communication overhead analysis and real-world deployment validation are required to fully characterize the framework's operational feasibility at scale. The current CMS implementation has been validated primarily through simulation and theoretical analysis, outperforming traditional satellite task-scheduling approaches in terms of task assignment and completion, throughput, priority management, and resource efficiency. However this validations incorporate limited real-world operational data to fully characterize performance under actual space mission conditions. Immediate validation efforts include real space mission testing of the CMS framework in two separate CubeSat missions scheduled for late 2025 and 2026, which will provide critical operational data and validate the framework's performance in actual space environments. Future technical developments should explore advanced ML integration into the CMS optimization process, including RL for dynamic constraint satisfaction and federated-learning approaches for multi-operator coordination. Furthermore, research into hybrid distributed agent-based architectures would address mega-constellation scalability by enabling satellite clusters to operate as intelligent agents while maintaining centralized coordination, combining global optimization benefits with distributed system resilience.