Modeling and Performance Analysis of Multi-layer Satellite Networks Based on STK

A multi-layered satellite network (MLSN) appears to be a promising network for providing global ubiquitous broadband communication. To utilize the abundant network resources of the MLSNs, fair traffic distribution among its satellite layers is, indeed, important. In this paper, we propose a routing method to optimally distribute traffic load among the layers (i.e., the satellite layers in the MLSN). The load balancing scheme of the proposed routing method is developed by adopting a traffic distribution model, which is based upon network capacity estimation and theoretical analysis of the congestion rate in each layer. The performance of the proposed routing method has been validated through extensive computer simulations, which demonstrate that our traffic distribution model is reliable enough to characterize the traffic behavior in the MLSN. Furthermore, in contrast with the basic routing approach, our proposed routing method is more effective in terms of improved throughput and lower packet drops, which are optimized by the theoretical parameter setting.

Multi-Layered Satellite Network (MLSN) is a kind of delay tolerant network with characters of intermittent connection, long packet queuing time, complex and uncertain delay time, etc. The call congestion rate of new business is higher in delay tolerant network which leads to the reduction of communication quality and the high packet loss rate. The existing traffic control scheme is hard to meet the individual requirements of new business. It becomes more and more important to study the most effective traffic control scheme so as to increase the satellite bandwidth utilization. According to the nuclear chain reaction of atomic physics, a novel traffic control method based on chain reaction model (TCCR) is presented in this paper to realize dynamic data transmission and control for MLSN. The optimal flow distribution scheme will be obtained by analyzing the network transmission information in this algorithm. Simulation by OPENT is done which proves that TCCR can obviously improve the network total throughput, shorten link transmission delay, and reduce packet loss rate of MLSN.

The formalization of multilayer networks allows for new ways to measure sociality in complex social systems, including groups of animals. The same mathematical representation and methods are widely applicable across fields and study systems, and a network can represent drastically different types of data. As such, in order to apply analyses and interpret the results in a meaningful way the researcher must have a deep understanding of what their network is representing and what parts of it are being measured by a given analysis. Multilayer social networks can represent social structure with more detail than is often present in single layer networks, including multiple “types” of individuals, interactions, or relationships, and the extent to which these types are interdependent. Multilayer networks can also encompass a wider range of social scales, which can help overcome complications that are inherent to measuring sociality. In this paper, I dissect multilayer networks into the parts that correspond to different components of social structures. I then discuss common pitfalls to avoid across different stages of multilayer network analyses—some novel and some that always exist in social network analysis but are magnified in multi-layer representations. This paper serves as a primer for building a customized toolkit of multilayer network analyses, to probe components of social structure in animal social systems.

In this study, voice over IP (VoIP) performance in multi-layered satellite IP networks with on-board processing (OBP) capability is investigated. With on-board processing, a satellite can process the received data, and according to the nature of application, it can decide on the transmission properties. Specifically, considered OBP includes error-correcting coding to combat adverse channel conditions and priority queueing mechanisms for efficient use of the system resources. After a brief overview of relevant aspects of satellite networks to VoIP, the VoIP performance for non-geostationary Earth orbit (NGEO) single layer and multi-layered constellations are examined using a network simulation tool. The outcome of these simulations justifies the premise for the multi-layered architecture for delay-sensitive and real-time traffic such as voice. Subsequently, the effects of some quality-of-service (QoS) mechanisms in multi-layered architecture are tested using various simulation parameters. The system simulation verifies that multi-layered satellite networks with OBP capabilities and QoS mechanisms are essential for feasibility of packet-based high quality voice services which are vital components of next-generation communications networks.

Traditional network traffic scheduling algorithms are difficult to accurately analyze and model. In the face of satellite networks with complex network structures, diverse transmission resources, and high dynamics, routing strategies lack flexibility. In order to reduce the possibility of network congestion in the multi-layer satellite network, a satellite network traffic scheduling algorithm based on multi-agent reinforcement learning is designed. By improving the experience pool and combining the priority sampling mechanism, the training speed and generalization ability of the model are improved. By comparing with the other four classic algorithms, we verified that this algorithm has a faster convergence speed and a better flow balance effect.

Satellite computing networks leverage the placement of computing resources on low-Earth orbit (LEO) satellite communication network to bring computing power closer to users, enabling robust and scalable solutions for emerging applications such as Internet of thing, edge computing and real-time analysis. However, with the explosion of multi-layer LEO mega-constellations(MLMCs), how to use the least number of computing nodes to achieve computing power resource coverage? This paper proposes a computing node deployment algorithm for MLMCs to construct satellite computing network with the minimum number of computing node. Specifically, we analyze the existence conditions of optimal computing node deployment, and derive the expression of the relationship between the number of computing nodes needed on the LEO, the multi-layer satellite network structure and the number of accessible hops of computing nodes. The expression can determine the minimum number of computing nodes needed to be placed on the MLMCs to realize optimal computing node deployment under a certain number of accessible hops. Then, according to the limitation of optimal computing node deployment, a multi-layer satellite network computing node deployment algorithm is proposed to determine the position of the computing resource in MLMCs, which can form a stable computing structure in satellite network. Finally, through simulation, the influence of network scale and computing node deployment on the number of required computing nodes and the effectiveness of signaling delay is analyzed. The simulation results show that this method can achieve better delay with the least number of computing nodes, balance the number of computing nodes and delay, and meet the specific network requirements.

Most work on convention propagation have focused on single-layer networks. This work a) highlights the need for studying convention propagation in multi-layer networks, b) presents results comparing the speed of convention propagation in single-layer vs. multi-layer networks and c) demonstrates the role of influencer agents in convention propagation in multi-layer networks.

The development of satellite networks is drawing much more attention in recent years due to the wide coverage ability. Composed of geosynchronous orbit (GEO), medium earth orbit (MEO), and low earth orbit (LEO) satellites, the satellite network is a three-layer heterogenous network of high complexity, for which comprehensive theoretical analysis is still missing. In this paper, we investigate the capacity performance of the three-layer heterogenous satellite network. We first construct the network model and the capacity model of the network with detailed design of the network parameters. Then, taking time structure into account, we propose a time structure based augmenting path searching method, which can significantly reduce the computing complexity. Finally, based on the model and method proposed, we analyze the capacity performance of the three-layer heterogenous satellite network with numerical results.

Satellite networks have many advantages over traditional terrestrial networks. However, it is very difficult to design a satellite network with excellent performance. The paper briefly summarizes some existing satellite network routing technologies from the perspective of both single-layer and multilayer satellite constellations, and focuses on the main ideas, characteristics, and existing problems of these routing technologies. For single-layer satellite networks, two routing strategies are discussed, virtual node strategy and virtual topology strategy. Moreover, considering the deficiency of existing multilayer satellite network routing, we discuss the topic invulnerability. Finally, the challenges and problems faced by the satellite network are analyzed and the trend of future development is predicted.

A novel prediction model of multi-layer symbolic pattern network: Based on causation entropy

Satellite networks have great potential in providing global ubiquitous broadband communication. In this paper, we explore the capacity of both single-layered and two-layered satellite networks. Closed-form approximate expressions of network capacity are derived, which provide insights into the impact of different network parameters, such as number of orbits, number of satellites in each orbit, link bandwidth, existence of the seam, the relative position between the two layers, etc. We investigate the advantages of two-layered structure by comparing the capacity of single-layered and two-layered networks. The results show that the network capacity of two-layered networks is always no less than the total capacity of the two layers. Moreover, we obtain the network parameter condition under which the network capacity of two-layered networks is strictly larger than the sum capacity of the two layers. The results obtained in this paper can serve as a guideline for the design of efficient multi-layered satellite networks.

Drawing topological properties from a multi-layered network: The case of an air transport network in “the Belt and Road” region

Purpose This paper aims to investigate the shock transmission structure among Chinese financial institutions. Design/methodology/approach Based on the market data of 34 listed financial institutions from 2011 to 2023, this study proposes the empirical mode decomposition into the generalized forecast variance decomposition framework to create multilayer networks that include volatility spillover layers at multidimensional time scales for analyzing the shock transmission structure among Chinese financial institutions. Findings Empirical research indicates that layers at different time scales exhibit distinct network structures, and multilayer network analysis provides a more comprehensive understanding of the financial system. The volatility spillover networks exhibit notable time-varying characteristics with significant periodic patterns linked to endogenous and exogenous shocks. The importance of financial institutions is dynamic. Key nodes in the multilayer spillover network mainly come from the banking and securities sectors, with banks being primarily joint-stock commercial banks and urban commercial banks and securities institutions controlled mainly by large central enterprises or local governments. Originality/value This paper explores how to use an adaptive decomposition method to more accurately construct multilayer networks for risk contagion and proposes a new “time-frequency domain” multilayer network analysis framework. By constructing multilayer networks at different time scales, the risk contagion patterns driven by different economic mechanisms are elucidated. Finally, this article provides a more refined and robust evaluation of the systemic importance of financial institutions from a dynamic network perspective with multiple time scales.

Satellite communication networks, as an effective complement to terrestrial communication networks, enable global coverage communications. The Low-orbit satellite network has the characteristics of low transmission delay, short operation period and global coverage communication, it has become a popular research direction in satellite communication networks. With the continuous development of digital and intelligent communication in the world, satellite network services continue to expand, and higher requirements are placed on network transmission performance. The traditional single-structure low-orbit satellite network using centralized routing has been unable to meet current needs.. Therefore, with the low-orbit satellite network as the core, people have conducted in-depth research on satellite communication network routing algorithms that meet the requirements of high-quality, high-efficiency and largecapacity communication. Especially in the three hot issues of low-orbit satellite network routing algorithms, a lot of research results have emerged, including: QoS-based artificial intelligence algorithm routing algorithm; multi-layer satellite network routing algorithm; satellite network multipath routing algorithm. Through the analysis and discussion of these problems and achievements, the idea of combining the three algorithms on demand is proposed, and the challenges are analyzed.

Objective: The investigation of brain functional network dynamics offers a promising approach to understanding network reorganization poststroke. This study aims to explore the dynamic network configurations associated with motor recovery in stroke patients and assess their predictive potential using multilayer network analysis. Methods: Resting-state functional magnetic resonance imaging data were collected from patients with subacute stroke within 2 weeks of onset and from matched healthy controls (HCs). Group-independent component analysis and a sliding window approach were utilized to construct dynamic functional networks. A multilayer network model was applied to quantify the switching rates of individual nodes, subnetworks, and the global network across the dynamic network. Correlation analyses assessed the relationship between switching rates and motor function recovery, while linear regression models evaluated the predictive potential of global network switching rate on motor recovery outcomes. Results: Stroke patients exhibited a significant increase in the switching rates of specific brain regions, including the medial frontal gyrus, precentral gyrus, inferior parietal lobule, anterior cingulate, superior frontal gyrus, and postcentral gyrus, compared to HCs. Additionally, elevated switching rates were observed in the frontoparietal network, default mode network, cerebellar network, and in the global network. These increased switching rates were positively correlated with baseline Fugl-Meyer assessment (FMA) scores and changes in FMA scores at 90 days poststroke. Importantly, the global network's switching rate emerged as a significant predictor of motor recovery in stroke patients. Conclusions: The reorganization of dynamic network configurations in stroke patients reveals crucial insights into the mechanisms of motor recovery. These findings suggest that metrics of dynamic network reorganization, particularly global network switching rate, may offer a robust predictor of motor recovery.