Evolution of High Throughput Satellite Systems: Vision, Requirements, and Key Technologies

High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G networks. HTS are mainly designed to provide higher data rates and capacities. Fueled by technological advancements including beamforming, advanced modulation techniques, reconfigurable phased array technologies, and electronically steerable antennas, HTS have emerged as a fundamental component for future network generation. This paper offers a comprehensive state-of-the-art of HTS systems, with a focus on standardization, patents, channel multiple access techniques, routing, load balancing, and the role of software-defined networking (SDN). In addition, we provide a vision for next-satellite systems that we named as extremely-HTS (EHTS) toward autonomous satellites supported by the main requirements and key technologies expected for these systems. The EHTS system will be designed such that it maximizes spectrum reuse and data rates, and flexibly steers the capacity to satisfy user demand. We introduce a novel architecture for future regenerative payloads while summarizing the challenges imposed by this architecture

Explicit Load Balancing Technique for NGEO Satellite IP Networks With On-Board Processing Capabilities

科研費報告書収録論文(課題番号:17500030/研究代表者:加藤寧/インターネットと高親和性を有する次世代低軌道衛星ネットワークに関する基盤研究

Centralized and Decentralized ML-Enabled Integrated Terrestrial and Non-Terrestrial Networks

Non-terrestrial networks (NTNs) are a critical enabler of the persistent connectivity vision of sixth-generation networks, as they can service areas where terrestrial infrastructure falls short. However, the integration of these networks with the terrestrial network is laden with obstacles. The dynamic nature of NTN communication scenarios and numerous variables render conventional model-based solutions computationally costly and impracticable for resource allocation, parameter optimization, and other problems. Machine learning (ML)-based solutions, thus, can perform a pivotal role due to their inherent ability to uncover the hidden patterns in time-varying, multi-dimensional data with superior performance and less complexity. Centralized ML (CML) and decentralized ML (DML), named so based on the distribution of the data and computational load, are two classes of ML that are being studied as solutions for the various complications of terrestrial and non-terrestrial networks (TNTN) integration. Both have their benefits and drawbacks under different circumstances, and it is integral to choose the appropriate ML approach for each TNTN integration issue. To this end, this paper goes over the TNTN integration architectures as given in the 3rd generation partnership project standard releases, proposing possible scenarios. Then, the capabilities and challenges of CML and DML are explored from the vantage point of these scenarios.Comment: This work was supported in part by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant No. 5200030 with the cooperation of Vestel and Istanbul Medipol Universit

Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G

Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th Generation (6G) networks, providing ubiquitous, continuous, and scalable services. Satellites emerge as the primary enabler for NTN, leveraging their extensive coverage, stable orbits, scalability, and adherence to international regulations. However, satellite-based NTN presents unique challenges, including long propagation delay, high Doppler shift, frequent handovers, spectrum sharing complexities, and intricate beam and resource allocation, among others. The integration of NTNs into existing terrestrial networks in 6G introduces a range of novel challenges, including task offloading, network routing, network slicing, and many more. To tackle all these obstacles, this paper proposes Artificial Intelligence (AI) as a promising solution, harnessing its ability to capture intricate correlations among diverse network parameters. We begin by providing a comprehensive background on NTN and AI, highlighting the potential of AI techniques in addressing various NTN challenges. Next, we present an overview of existing works, emphasizing AI as an enabling tool for satellite-based NTN, and explore potential research directions. Furthermore, we discuss ongoing research efforts that aim to enable AI in satellite-based NTN through software-defined implementations, while also discussing the associated challenges. Finally, we conclude by providing insights and recommendations for enabling AI-driven satellite-based NTN in future 6G networks.Comment: 40 pages, 19 Figure, 10 Tables, Surve

Optimization of intersatellite routing for real-time data download

The objective of this study is to develop a strategy to maximise the available bandwidth to Earth of a satellite constellation through inter-satellite links. Optimal signal routing is achieved by mimicking the way in which ant colonies locate food sources, where the 'ants' are explorative data packets aiming to find a near-optimal route to Earth. Demonstrating the method on a case-study of a space weather monitoring constellation; we show the real-time downloadable rate to Earth

Self-Evolving Integrated Vertical Heterogeneous Networks

6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services and use cases while dealing with network complexity efficiently. Such enhanced network agility will require developing a self-evolving capability in designing both the network architecture and resource management to intelligently utilize resources, reduce operational costs, and achieve the coveted quality of service (QoS). To enable this capability, the necessity of considering an integrated vertical heterogeneous network (VHetNet) architecture appears to be inevitable due to its high inherent agility. Moreover, employing an intelligent framework is another crucial requirement for self-evolving networks to deal with real-time network optimization problems. Hence, in this work, to provide a better insight on network architecture design in support of self-evolving networks, we highlight the merits of integrated VHetNet architecture while proposing an intelligent framework for self-evolving integrated vertical heterogeneous networks (SEI-VHetNets). The impact of the challenges associated with SEI-VHetNet architecture, on network management is also studied considering a generalized network model. Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are identified. Finally, the potential future research directions for advancing the autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table

Energy efficiency in LEO satellite and terrestrial wired environments

To meet an ever-growing demand for advanced multimedia services and to support electronic connectivity anywhere on the planet, development of ubiquitous broadband multimedia systems is gaining a huge interest at both academic and industry levels. Satellite networks in general and LEO satellite constellations in particular will play an essential role in the deployment of such systems. Therefore, as LEO satellite constellations like Iridium or IridiumNEXT are extremely expensive to deploy and maintain, extending their service lifetimes is of crucial importance. In the main part of this thesis, we propose different techniques for extending satellite service life in LEO satellite constellations. Satellites in such constellations can spend over 30% of their time under the earth’s umbra, time during which they are powered by batteries. While the batteries are recharged by solar energy, the Depth of Discharge (DoD) they reach during eclipse significantly affects their lifetime – and by extension, the service life of the satellites themselves. For batteries of the type that power Iridium and Iridium-NEXT satellites, a 15% increase to the DoD can practically cut their service lives in half. We first focus on routing and propose two new routing metrics – LASER and SLIM – that try to strike a balance between performance and battery DoD in LEO satellite constellations. Our basic approach is to leverage the deterministic movement of satellites for favoring routing traffic over satellites exposed to the sun as opposed to the eclipsed satellites, thereby decreasing the average battery DoD– all without taking a significant penalty in performance. Then, we deal with resource consolidation – a new paradigm for the reduction of the power consumption. It consists in having a carefully selected subset of network links entering a sleep state, and use the rest to transport the required amount of traffic. This possible without causing major disruptions to network activities. Since communication networks are designed over the peak traffic periods, and with redundancy and over-provisioned in mind. As a remedy to these issues, we propose two different methods to perform resource consolidation in LEO networks. First, we propose trafficaware metric for quantifiying the quality of a frugal topology, the Maximum Link Utilization (MLU). With the problem being NP-hard subject to a given MLU threshold, we introduce two heuristics, BASIC and SNAP, which represent different tradeoffs in terms of performance and simplicity. Second, we propose a new lightweight traffic-agnostic metric for quantifiying the quality of a frugal topology, the Adequacy Index (ADI). After showing that the problem of minimizing the power consumption of a LEO network subject to a given ADI threshold is NP-hard, we propose a heuristc named AvOId to solve it. We evaluate both forms of resource consolidation using realistic LEO topologies and traffic requests. The results show that the simple schemes we develop are almost double the satellite batteries lifetime. Following the green networking in LEO systems, the second part of this thesis focuses on extending the resource consolidation schemes to current wired networks. Indeed, the energy consumption of wired networks has been traditionally overlooked. Several studies exhibit that the traffic load of the routers only has a small influence on their energy consumption. Hence, the power consumption in networks is strongly related to the number of active network elements. In this context, we extend the traffic-agnostic metric, ADI, to the wired networks. We model the problem subject to ADI threshold as NP-hard. Then, we propose two polynomial time heuristics – ABStAIn and CuTBAck. Although ABStAIn and CuTBAck are traffic unaware, we assess their behavior under real traffic loads from 3 networks, demonstrating that their performance are comparable to the more complex traffic-aware solutions proposed in the literature

Multi-objective resource optimization in space-aerial-ground-sea integrated networks

Space-air-ground-sea integrated (SAGSI) networks are envisioned to connect satellite, aerial, ground, and sea networks to provide connectivity everywhere and all the time in sixth-generation (6G) networks. However, the success of SAGSI networks is constrained by several challenges including resource optimization when the users have diverse requirements and applications. We present a comprehensive review of SAGSI networks from a resource optimization perspective. We discuss use case scenarios and possible applications of SAGSI networks. The resource optimization discussion considers the challenges associated with SAGSI networks. In our review, we categorized resource optimization techniques based on throughput and capacity maximization, delay minimization, energy consumption, task offloading, task scheduling, resource allocation or utilization, network operation cost, outage probability, and the average age of information, joint optimization (data rate difference, storage or caching, CPU cycle frequency), the overall performance of network and performance degradation, software-defined networking, and intelligent surveillance and relay communication. We then formulate a mathematical framework for maximizing energy efficiency, resource utilization, and user association. We optimize user association while satisfying the constraints of transmit power, data rate, and user association with priority. The binary decision variable is used to associate users with system resources. Since the decision variable is binary and constraints are linear, the formulated problem is a binary linear programming problem. Based on our formulated framework, we simulate and analyze the performance of three different algorithms (branch and bound algorithm, interior point method, and barrier simplex algorithm) and compare the results. Simulation results show that the branch and bound algorithm shows the best results, so this is our benchmark algorithm. The complexity of branch and bound increases exponentially as the number of users and stations increases in the SAGSI network. We got comparable results for the interior point method and barrier simplex algorithm to the benchmark algorithm with low complexity. Finally, we discuss future research directions and challenges of resource optimization in SAGSI networks