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Robust Beam-to-Satellite Routing Strategies for Megaconstellations
Abstract
Robust routing strategies for satellite constellations are crucial for mitigating outage probability amidst satellite failures. While existing literature primarily addresses robust routing within the satellite network, this study introduces innovative strategies to enhance robustness in ground-satellite links. Leveraging established heuristic and optimization methods, these strategies are tailored to incorporate redundancy. Using the Starlink constellation with 200,000 users as an example, our findings illustrate a significant reduction in outage probability for end-users, albeit with a trade-off of reduced capacity. Specific approaches demonstrate the capability to sustain a substantial portion of throughput, maintaining 96% while reducing the outage probability up to 66% and 10% for internal and external failures, respectively.
... The proposed Q-learning solution demonstrates comparable delays to centralized algorithms under steady-state conditions, increased supported traffic load without congestion, and minimal signaling overhead among satellites. Another significant contribution is the work on robust beam-to-satellite routing strategies for mega-constellations [287]. Aiming to minimize end-to-end latency and maximize the supported traffic load, strategies are proposed to address the challenges of routing in the presence of highly imbalanced traffic and dynamic network topology. ...
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.
For mega satellite constellations, it has been a great challenge to achieve global routing and guarantee the performance of inter‐satellite transmission. To address the problem, a robust routing strategy based on deep reinforcement learning is proposed in this letter. The proposed method is applicable to degraded transmission performance, which exhibits better conformity to real‐world scenarios. Moreover, the age of information (AoI) of packets are utilized as one of the multi‐optimization targets to ensure the effectiveness of message transmission throughout the network. Numerical simulations show the outstanding average AoI performance of the proposed method and its greater robustness to jamming compared to existing methods. Meanwhile, it is also more effective for utilizing resources.
Plain Language Summary
The operating satellites in low‐Earth orbit give the rapid information transfer between the satellites and the Earth. At the same time, these satellites are continuously slowed down and affected by the dense atmosphere of the Earth, which is referred to as the atmospheric drag. This effect can be greater during space weather events such as geomagnetic storms. Over the past years, thousands of Starlink satellites have been deployed by the SpaceX company into low‐Earth orbit. However, on 4th February, 38 Starlink satellites were destroyed before they were lifted to a higher Earth orbit, which brought an economic loss estimated to be several tens of millions of dollars. Geomagnetic indices indicated two successive geomagnetic storms, which could warm the upper atmosphere and increase the atmospheric drag. In this work, we provide a comprehensive review on the process of space weather during this event from the Sun all the way to the terrestrial atmosphere. We have illustrated the solar eruption, solar wind propagation, and atmospheric density enhancement, using both observed data and model simulations. This study calls for more accurate modeling and better understanding of space weather as well as collaborations between industry and space weather community.
Ultra-dense low earth orbit (LEO) satellite networks (LEO-UDSNs) are becoming a foundational component of future 5G and beyond. LEO-UDSNs bring unprecedented high-speed mobility, broadband capacity, and ultra-density qualities, introducing a significant challenge to traditional user association (UA) methods. In this article, we first discuss the difficulties in sequential UA optimization for scalable LEO-UDSNs. A specific multi-objective UA problem was formulated for the overall service efficiency. We initially proposed a practical Iterative Subgradient Algorithm (MOISA) decoupling potential trade-offs between limited equivalent power and availability relationship, rapidly obtaining solutions in stable cases. Second, we proposed a Multi-Objective Dynamic User Association Approach (MODUAA), inheriting the MOISA with stability enhancements for sequential optimizations, ensuring the performance requirements, and alleviating the iteration redundancy in dynamic scenarios. Finally, the outperforming of the MOISA and the applicability of the MODUAA were elaborated in various comparisons.
Satellite-terrestrial integrated networks (STINs) are considered to be a new paradigm for the next generation of global communication because of its distinctive merits, such as wide coverage, high reliability, and flexibility. When the satellite associates with different base stations (BSs) and adopts different channels for communication, the utility of offloading data to BSs is different. In our work, we study how to jointly associate satellites with appropriate BSs and allocate channels to satellites. Our purpose is to maximize the utility of the data offloaded from satellites to BSs while considering the load balance of BSs. However, some satellites are often unable to connect to BSs because of their periodic flight characteristic, which makes the joint satellite-BS association and channel allocation more challenging. To solve the problem that satellites sometimes cannot connect to BSs, we abstract the communication model between satellites and BSs into a bipartite graph and add a virtual BS to ensure that all satellites can connect to at least one BS. Then, in the constructed joint optimization problem, we solve the assignment of satellites and channels simultaneously. Considering that the joint optimization problem is nonconvex, we use double deep Q-Network (DDQN) for achieving the optimal strategy of satellite association and channel allocation. Furthermore, the reward value in most state transition information generated by satellites is 0, which leads to the low learning efficiency of DDQN. Aiming at enhancing the learning efficiency of DDQN, the priority sampling-based DDQN (PSDDQN) algorithm is proposed. Experimental results demonstrate that PSDDQN gets better utility and achieves the load balance of BSs compared with other algorithms.
With the reduced distance between satellites in modern megaconstellations, the potential for self-interference has emerged as a critical challenge that demands strategic solutions from satellite operators. The goal of this paper is to propose a cooperative framework that combines the Satellite Routing (i.e., mapping of beams to satellites) and Frequency Assignment (i.e., mapping of frequency spectrum to beams) strategies to mitigate self-interference both within and between satellites. This approach stands in contrast to current practices found in the literature, which address each problem independently and solely focus on intra-satellite interference. This study presents a novel methodology for addressing the Satellite Routing problem, specifically tailored for modern constellations to maximize capacity while effectively mitigating self-interference through the use of Integer Optimization. By combining this method with established Frequency Assignment techniques, the results demonstrate an increase in throughput of up to 138% for constellations such as SpaceX Starlink. Notably, the study reveals that relying on individual approaches to tackle interference may lead to undesired outcomes, underscoring the advantages of a cooperative framework. Through simulations, the study highlights the practicality and applicability of the proposed method under realistic operational conditions.
The advancement of space technology has positioned LEO satellite networks as a pivotal solution for global digitalization. However, satellites can fail due to various internal and external threats. Their limited buffer capacity and processing ability can exacerbate failures, posing a risk of cascading failures to the entire network. To delve deeper into the cascading process of LEO satellite networks, we have developed a dynamic cascading failure model. We propose a method to calculate the minimum time required for satellite movement to cover discrete zone units on Earth. By considering the dynamic changes in satellite coverage, we model the probability of user data reaching satellites using a Poisson process. Additionally, we have established models for satellites’ processing ability and buffer capacity to depict a more realistic satellite state transition. Satellites can become congested or even overloaded when they receive an excessive amount of data and require a corrective phase before returning to standard operations. We present a dynamic cascading algorithm, ensuring that the sequence of observations doesn’t impact the final cascading process outcome. Through detailed case studies involving three constellations, we found that cascading failures tend to propagate mainly to satellites within two hops. Smaller constellations are more susceptible to periodic cascading failures, while mega-constellations might experience rapid, severe avalanche effects. These findings offer practical insights for satellite network managers aiming to mitigate cascading risks.
In recent years, low earth orbit (LEO) satellite networks (LSNs) have attracted increasing attention due to economic prospect and advantages in high bandwidth and low latency. In order to provide higher quality of service (QoS) and address the frequent handover problem among LEO satellites, we propose a user-centric handover scheme for ultra-dense LSNs in this letter. Our basic idea is to exploit satellite’s storage capability to improve user’s communication quality. By buffering user’s downlink data in multiple satellites simultaneously, the terrestrial user can realize seamless handover and always access the satellite with the best link quality. Simulation results show that our user-centric handover scheme outperforms the traditional handover scheme in terms of throughput, handover delay and end-to-end latency.
Power consumption is a major limitation in the downlink of multibeam satellite systems, since it has a significant impact on the mass and lifetime of the satellite. In this context, we study a new energy-aware power allocation problem that aims to jointly minimize the unmet system capacity (USC) and total radiated power by means of multi-objective optimization. First, we transform the original nonconvex-nondifferentiable problem into an equivalent nonconvex-differentiable form by introducing auxiliary variables. Subsequently, we design a successive convex approximation (SCA) algorithm in order to attain a stationary point with reasonable complexity. Due to its fast convergence, this algorithm is suitable for dynamic resource allocation in emerging on-board processing technologies. In addition, we formally prove a new result about the complexity of the SCA method, in the general case, that complements the existing literature where the complexity of this method is only numerically analyzed.
The demand for satellite communication (SATCOM) has been increasing to provide communication environments for terminals in areas with no terrestrial network coverage and to provide communication means with Internet of Things (IoT) equipment. Accordingly, a few schemes are being studied to efficiently allocate power resources with multi-beam directivity control through digital beam forming (DBF). Moreover, power resource allocation with simultaneous control of the transmit power and multi-beam directivity can be expected to increase the flexibility according to traffic demand. However, there is a trade-off between the transmit power and beam directivity under some physical constraints, and currently there is no mathematical model to solve this problem. The contributions of this paper are to construct a new mathematical model necessary for solving the trade-off, and to introduce a novel power resource allocation with fusion control of the transmit power and multi-beam directivity according to traffic demand with the objective of improving the allocation efficiency of communication resources. We carried out a numerical analysis to verify the effectiveness of our proposal.
The fast development in the production of small, low-cost satellites is propelling an important increase in satellite mission planning and operations projects. Central to satellite mission planning is the resolution of scheduling problem for an optimised allocation of user requests for efficient communication between operations teams at the ground and spacecraft systems. The aim of this paper is to survey the state of the art in the satellite scheduling problem, analyse its mathematical formulations, examine its multi-objective nature and resolution through meta-heuristics methods. Finally, we consider some optimisation problems arising in spacecraft design, operation and satellite deployment systems.
This letter proposes a graph-based satellite handover framework for the low earth orbit (LEO) satellite communication networks. In order to maintain the connection with the communicating counterpart, the user has to switch between consecutive satellites covering the very user. A directed graph with covering period of the satellite as its node, and the link representing the possible handover between two overlapping periods, is calculated in advance, and then, the satellite handover process can be viewed as finding a path in the directed graph. By setting the link weight according to different handover criterions, the graph-based framework can support a variety of satellite handover strategies, which shows the improved flexibility over the existing research. The application of the framework to find the shortest handover path conducted on two typical LEO satellite networks, viz., Iridium and Globalstar, also corroborates the effectiveness of the proposed handover framework.
Dynamic frequency assignment for mobile users in multibeam satellite constellations
- Casadesus
Dynamic frequency assignment for mobile users in multibeam satellite constellations
- G Casadesus
- J J G Luis
- N Pachler
- E F Crawley
- B G Cameron