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

Stochastic Geometry-Based Low Latency Routing in Massive LEO Satellite Networks

In this paper, the routing in massive low earth orbit (LEO) satellite networks is studied. When the satellite-to-satellite communication distance is limited, we choose different relay satellites to minimize the latency in a constellation at a constant altitude. Firstly, the global optimum solution is obtained in the ideal scenario when there are available satellites at all the ideal locations. Next, we propose a nearest neighbor search algorithm for realistic (non-ideal) scenarios with a limited number of satellites. The proposed algorithm can approach the global optimum solution under an ideal scenario through a finite number of iterations and a tiny range of searches. Compared with other routing strategies, the proposed algorithm shows significant advantages in terms of latency. Furthermore, we provide two approximation techniques that can give tight lower and upper bounds for the latency of the proposed algorithm, respectively. Finally, the relationships between latency and constellation height, satellites' number, and communication distance are investigated

Distributed Probabilistic Congestion Control in LEO Satellite Networks

Satellite communication in Low Earth Orbiting (LEO) constellations is an emerging topic of interest. Due to the high number of LEO satellites in a typical constellation, a centralized algorithm for minimum-delay packet routing would incur significant signaling and computational overhead. We can exploit the deterministic topology of the satellite constellation to calculate the minimum-delay path between any two nodes in the satellite network. But that does not take into account the traffic information at the nodes along this minimum-delay path. We propose a distributed probabilistic congestion control scheme to minimize end-to-end delay. In the proposed scheme, each satellite, while sending a packet to its neighbor, adds a header with a simple metric indicating its own congestion level. The decision to route packets is taken based on the latest traffic information received from the neighbors. We build this algorithm onto the Datagram Routing Algorithm (DRA), which provides the minimum delay path, and the decision for the next hop is taken by the congestion control algorithm. We compare the proposed congestion control mechanism with the existing congestion control used by the DRA via simulations, and show improvements over the same.Comment: 9 pages, 10 figures, conferenc

Location Management in IP-based Future LEO Satellite Networks: A Review

Future integrated terrestrial, aerial, and space networks will involve thousands of Low Earth Orbit (LEO) satellites forming a network of mega-constellations, which will play a significant role in providing communication and Internet services everywhere, at any time, and for everything. Due to its very large scale and highly dynamic nature, future LEO satellite networks (SatNets) management is a very complicated and crucial process, especially the mobility management aspect and its two components location management and handover management. In this article, we present a comprehensive and critical review of the state-of-the-art research in LEO SatNets location management. First, we give an overview of the Internet Engineering Task Force (IETF) mobility management standards (e.g., Mobile IPv6 and Proxy Mobile IPv6) and discuss their location management techniques limitations in the environment of future LEO SatNets. We highlight future LEO SatNets mobility characteristics and their challenging features and describe two unprecedented future location management scenarios. A taxonomy of the available location management solutions for LEO SatNets is presented, where the solutions are classified into three approaches. The "Issues to consider" section draws attention to critical points related to each of the reviewed approaches that should be considered in future LEO SatNets location management. To identify the gaps, the current state of LEO SatNets location management is summarized. Noteworthy future research directions are recommended. This article is providing a road map for researchers and industry to shape the future of LEO SatNets location management.Comment: Submitted to the Proceedings of the IEE

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

Unattended network operations technology assessment study. Technical support for defining advanced satellite systems concepts

The results are summarized of an unattended network operations technology assessment study for the Space Exploration Initiative (SEI). The scope of the work included: (1) identified possible enhancements due to the proposed Mars communications network; (2) identified network operations on Mars; (3) performed a technology assessment of possible supporting technologies based on current and future approaches to network operations; and (4) developed a plan for the testing and development of these technologies. The most important results obtained are as follows: (1) addition of a third Mars Relay Satellite (MRS) and MRS cross link capabilities will enhance the network's fault tolerance capabilities through improved connectivity; (2) network functions can be divided into the six basic ISO network functional groups; (3) distributed artificial intelligence technologies will augment more traditional network management technologies to form the technological infrastructure of a virtually unattended network; and (4) a great effort is required to bring the current network technology levels for manned space communications up to the level needed for an automated fault tolerance Mars communications network

An intelligent call admission control algorithm for load balancing in 5G-satellite networks

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Cellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different RATs integrated. In addition to 5G, the user’s device (UD) will be able to connect to the network via LTE, WiMAX, Wi-Fi, Satellite, and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. Satellite networks are among the most sophisticated communication technologies which offer specific benefits in geographically dispersed and dynamic networks. Utilising their inherent advantages in broadcasting capabilities, global coverage, decreased dependency on terrestrial infrastructure, and high security, they offer highly efficient, effective, and rapid network deployments. Satellites are more suited for large-scale communications than terrestrial communication networks. Due to their extensive service coverage and strong multilink transmission capabilities, satellites offer global high-speed connectivity and adaptable access systems. The convergence of 5G technology and satellite networks therefore marks a significant milestone in the evolution of global connectivity. However, this integration introduces a complex problem related to resource management, particularly in Satellite – Terrestrial Integrated Networks (STINs). The key issue at hand is the efficient allocation of resources in STINs to enhance Quality of Service (QoS) for users. The root cause of this issue originates from a vast quantity of users sharing these resources, the dynamic nature of generated traffic, the scarcity of wireless spectrum resources, and the random allocation of wireless channels. Hence, resource allocation is critical to ensure user satisfaction, fair traffic distribution, maximised throughput, and minimised congestion. Achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This research endeavours to address this challenge through the development and evaluation of an intelligent call admission control (CAC) algorithm based on Enhanced Particle Swarm Optimization (EPSO). The primary aim of this research is to design an EPSO-based CAC algorithm tailored specifically for 5G-satellite heterogeneous wireless networks. The algorithm's objectives include maximising the number of admitted calls while maintaining Quality of Service (QoS) for existing users, improving network resource utilization, reducing congestion, ensuring fairness, and enhancing user satisfaction. To achieve these objectives, a detailed research methodology is outlined, encompassing algorithm development, numerical simulations, and comparative analysis. The proposed EPSO algorithm is benchmarked against alternative artificial intelligence and machine learning algorithms, including the Artificial Bee Colony algorithm, Simulated Annealing algorithm, and Q-Learning algorithm. Performance metrics such as throughput, call blocking rates, and fairness are employed to evaluate the algorithms' efficacy in achieving load-balancing objectives. The experimental findings yield insights into the performance of the EPSO-based CAC algorithm and its comparative advantages over alternative techniques. Through rigorous analysis, this research elucidates the EPSO algorithm's strengths in dynamically adapting to changing network conditions, optimising resource allocation, and ensuring equitable distribution of traffic among different RATs. The result shows the EPSO algorithm outperforms the other 3 algorithms in all the scenarios. The contributions of this thesis extend beyond academic research, with potential societal implications including enhanced connectivity, efficiency, and user experiences in 5G-Satellite heterogeneous wireless networks. By advancing intelligent resource management techniques, this research paves the way for improved network performance and reliability in the evolving landscape of wireless communication

TRAFFIC ENGINEERING ALGORITHMS FOR SOFTWARE DEFINED SATELLITE-TERRESTRIAL NETWORKS

The telecom industry foresaw a constant change over the decades. Alongside the standardization of 5G, not only the traffic demand increased, but also the different requirements of the provided services, e.g., latency, traffic load and pattern, datarate. To match this trend, the traditional telecom infrastructure has been revolutionized, going from the "one-size-fits-all" model to a shared approach, where the infrastructure is shared among enterprise customers with even very different requirements. In this context, a paradigm, named network slicing, has considerably attracted the network operators. Network slicing is a successful enabling model for the 5G and beyond networks because it allows operators to tailor their networks based on the end use case, release unused functionalities and resources and dynamically assign them to different customers. In addition, 6G networks are advertised to bring the terrestrial and satellite networks closer, to work in a coordinated way and provide ubiquitous, heterogenous and reliable services. In this context, this thesis investigates the optimization of network slicing in an integrated satellite-terrestrial network. Well-known enabling technologies for network slicing, such as Software-Defined Networking (SDN), are included as a proof-of-concept SDN-based testbed to demonstrate and support the proposed optimization algorithms. As network slicing is a resource allocation problem, where virtualized resources are accommodated on the substrate network, we investigate this optimization problem that is well-known in the literature as Virtual Network Embedding (VNE). Firstly, we study the application of VNE to an integrated Medium Earth Orbit (MEO)terrestrial network with the objective of minimizing the traffic migrations, considering the existence of inter-satellite links (ISLs) too. As satellite handovers are unavoidable, we showed as including the minimization of traffic handovers in the objective function, brings to a gain in terms of traffic migrations and packet loss up to 2.5-5% compared to the traditional approaches. Secondly, we investigated the benefit of flexibly accommodating traffic demands without fully assigning the required resources while keeping the user satisfaction probability (USP) under control. Thanks to the SDN-based testbed, the traffic is generated and the statistics are collected to real-time match the need of each user. This showed an increase in the acceptance ratio up to 11% compared to the baselines. Lastly, as the previous two main chapters investigated the point-to-point connectivity, we expanded the work to the embedding of full slices for a combined Geostationary (GEO)-Low Earth Orbit (LEO) satellites and terrestrial network. We provided a flexible framework, for 6G use-cases with real network requirements, which operates based on prioritization, minimizes the migrations of slices when congestions occur over the substrate network and proactively manages the satellite handovers for each slice. Finally, we discuss conclusive remarks and future research directions