1,720,963 research outputs found
Integration of Satellites into 5G Eco-systems
No full textThe Fifth-Generation of Mobile Communications (5G) is intended to satisfy the growing
needs of users which can be summarised in the ability to access good quality services
anywhere and at any time. Those needs can be supported by the integration of
satellites in 5G systems due to the unique characteristics of satellites in terms of
higher coverage, reliability, and availability. In particular, Low Earth Orbit (LEO)
satellite constellations offer an appealing approach for supporting and complementing
Fifth-Generation of Mobile Communications (5G) New Radio (NR) communications
have the advantages of low propagation delay and low energy consumption
which makes them the best candidates for direct access 5G Non-Terrestrial Networks
(NTN). However, the major problem of LEO satellites is their higher speed relative
to the terrestrial mobile terminals, which causes mobile users to hand over between
satellites which has a negative impact on users’ Quality of Service (QoS) if occurs in
high frequency. Moreover, 5G communication technologies are designed to support a
wide spectrum of applications, including Artificial Intelligence, Virtual Reality, and the
Internet of Things (IoT). Thus, differentiating User Equipments (UEs) with different
and varying Traffic-Profiles (TP) has become necessary due to each application’s unique
performance requirements. Complicating matters further, LEO satellites operate with
limited onboard resources, including energy and channel resources. Thus a satellite
handover management strategy is needed to tackle all the above challenges. To tackle these challenges, we propose innovative LEO Satellite Handover management
strategies. These strategies mark a groundbreaking advancement by accounting for
application diversity per user and addressing the limited energy resources of LEO
satellites. Notably, these strategies successfully minimize the number of HOs, achieving
a zero blocking rate while effectively balancing the load among satellites.
On the other hand, to minimize blind exploitation of new systems, new technologies
should be verified and enhanced before being implemented to reduce the required cost
and time. In this context, we implemented an open-source System Level Simulator
(SLS) built on the foundation of the Network Simulator 3 (NS-3). This tool enables
the simulation of 5G Satellite-Terrestrial Integrated Networks (STIN) and surpasses
existing solutions by supporting Non-Terrestrial Networks (NTN) handover decisions,
dynamic BandWidth Part (BWP) selection, and Component Carrier (CC) configurations
tailored to different traffic profiles
Energy-Aware Satellite Handover Based on Deep Reinforcement Learning
No full textMultiple Low Earth Orbit (LEO) satellites have been deployed in constellations to provide User Equipments (UE)s with direct Internet connection at all times and from any location. UEs experience several handovers (HO)s during their service period due to the high speed of LEO satellites, which has a bad influence on UEs’ Quality of Service (QoS) if occurred extensively. Furthermore, next-generation communication technologies are intended to serve a broad range of applications each with unique performance needs, thus distinguishing UEs with diverse and varied Traffic-Profiles (TP) has become necessary. Moreover, LEO satellites have limited onboard resources (e.g., energy and channel resources), and the deployed constellations ensure that each UE is covered by more than one LEO satellite at any time, making it difficult to pick the best satellite at each time to provide the best QoS. Therefore, a satellite HO strategy has to effectively use the limited available satellite resources and prevent network congestion while respecting the various TPs per UE. To address the aforementioned challenges, we propose a Load Balancing Energy Aware Satellite Handover (LBEASH) strategy, that is the first in the state of the art to address the limited energy resource of LEO satellites and the variety of UEs’ performance requirements. The proposed LBEASH showcases significant achievements by avoiding unnecessary HOs and achieving a zero blocking rate while balancing the load among the satellites
Network Simulations for Non-Terrestrial Networks: Overcoming Deployment Challenges and Advancing System Optimization
No full textNon-Terrestrial Networks (NTNs) including satellites, Unmanned Aerial Vehicles (UAVs), and High Altitude Platforms (HAPS), are increasingly seen as essential for extending coverage and enhancing reliability in modern communication systems, particularly in the context of the growing demand driven by IoT and V2X applications. However, their deployment faces significant technical challenges, such as path loss, atmospheric conditions, and Doppler effects. To address these issues, standardization bodies and academic researchers are actively investigating novel architectures to optimize NTN communications. However, the high costs associated with satellite launches, infrastructure deployment, and large-scale testing make physical experimentation infeasible in most cases. Moreover, real-world tests lack the flexibility to assess a wide range of configurations under controlled conditions. To address these limitations, network simulations have become an essential tool for evaluating NTN performance, testing new protocols, and optimizing system parameters before deployment. This paper presents a novel NS3-based simulation tool that integrates advanced energy and channel models from 3GPP TR 38.811, accounting for diverse environmental settings (dense urban, suburban, rural) and atmospheric effects to ensure accurate SNR computations. The simulator also features a sophisticated mobility model for representing satellite constellations and ground user mobility, including pedestrians, vehicles, and airplanes. A case study is presented, demonstrating the simulator’s application in the development and performance evaluation of an energy-aware satellite handover strategy. This work highlights the critical role of simulation tools in advancing NTN technologies and supporting the development of next-generation communication
standards
Evaluating Performance in Satellite Communication Networks: An NS3-Based Simulation Study
No full textNext-generation communication technologies aim to meet the evolving demands of users, emphasizing seamless access to high-quality services regardless of location or time constraints. However, conventional ground-based networks face limitations in providing Internet connectivity to users on several moving platforms, such as airplanes, ships, and trains, as well as in remote areas where building an extensive terrestrial infrastructure is economically unfeasible. To address these challenges, researchers and standardization organizations, such as the ITU, ETSI, ESA, and 3GPP, are exploring the integration of satellites into communication systems. Satellites are appealing thanks to their unique capabilities in delivering reliable connectivity across diverse geographical regions, independent of environmental factors and events, such as climate or natural disasters. However, a thorough investigation and verification is essential to deal with several aspects and allow testing the developed solutions in controlled environment before integrating them into operational systems. Accurate simulation tools play a crucial role in modeling propagation environments and network dynamics to ensure effective deployment and optimization of satellite communication networks. This paper presents a Network Simulator 3 (NS3)-based simulation tool that encompasses several key functionalities to ensure an accurate modeling of communications in Satellite-Terrestrial Integrated Networks (STIN). Among the offered functionalities, the simulator includes a mo-bility model based on the NORAD Simplified General Perturbations 4 (SGP4) mathematical model to simulate Low Earth Orbit (LEO) satellite movements, mobility models tailored for ground users (pedestrian, vehicles, train, and airplanes), and channel models, based on the 3GPP's Technical Report (TR) 38.811, representing different environments (dense urban, urban, sub-urban, and rural) including atmospheric absorption and clutter effects to provide accurate estimations of signal propagation and Signal-to-Noise Ratio (SNR) calculations. This paper also presents a comprehensive study evaluating the performance in different network settings with a focus on satellite-to-ground SNR and maximum link capacity calculations across Ka- and S-bands, showcasing the effectiveness of using NS3 as a simulation platform to assess performance in LEO satellite networks. By integrating factors such as node positioning, channel modeling, and environmental influences, we offer valuable insights into the design and optimization of satellite communication systems for diverse deployment scenarios, aiming to narrow the gap between simulation and real-world deployment and paving the path for more efficient and resilient satellite communication networks in the future
NS-3-based 5G Satellite-Terrestrial Integrated Network Simulator
No full textThe Fifth-Generation of Mobile Communications (5G) is intended to satisfy the growing needs of users which can be summarised in the ability to access good quality services anywhere and at any time. Those needs can be supported by the integration of satellites in 5G systems due to the unique characteristics of satellites in terms of higher coverage, reliability, and availability. To minimize blind exploitation of new systems, new technologies should be verified and enhanced before being implemented to reduce the required cost and time. Creating a System Level Simulator (SLS) is an excellent way to evaluate new ideas and test novel systems. In this work, we present an open-source SLS based on the software Network Simulator 3 (NS-3). This tool allows simulating 5G Satellite-Terrestrial Integrated Networks (STIN) and overcomes the state-of-the-art solutions by supporting Non-Terrestrial Networks (NTN) handover decisions, dynamic BandWidth Part (BWP) selection, and Component Carrier (CC) configurations based on different defined traffic profiles
Reinforcement Learning-Based Load Balancing Satellite Handover Using NS-3
Full text linkThe Fifth-Generation of Mobile Communications (5G) is intended to meet users' growing needs for high-quality services at any time and from any location. The unique features of Low Earth Orbit (LEO) satellites in terms of higher coverage, reliability, and availability, can help expand the reach of 5G and beyond technologies to support those needs. However, because of their high speeds, a single LEO satellite is unable to provide continuous service to multiple User Equipments (UEs) spread over a large (potentially worldwide) area, resulting in the need for LEO satellite constellations with a high number of satellites and a consequent high amount of satellite handovers (HOs). Moreover, UEs can only acquire partial information about the satellite system and compete for the limited available communication resources of the satellites, requiring the implementation of a decentralized satellite HO strategy to avoid network congestion. In this paper, we propose a decentralized Load Balancing Satellite HO (LBSH) strategy based on multi-agent reinforcement Q-learning, implemented within the software Network Simulator 3 (NS-3). LBSH aims to reduce the total number of HOs and the blocking rate while balancing the load distribution among satellites. Our results show that the proposed LBSH method outperforms the state-of-the-art methods in terms of a 95% drop in the average number of HOs per user and an 84% reduction in blocking rate
User Centric Satellite Handover for Multiple Traffic Profiles Using Deep Q-Learning
No full textMultiple Low Earth Orbit (LEO) satellites have recently been launched in constellations to insure direct Internet access to users anywhere and at any time. Due to the high-speed mobility of LEO satellites, users undergo multiple handovers (HO)s during their service time, which has a negative impact on users' Quality of Service (QoS) if occurred in high frequency. Moreover, next-generation communication technologies are designed to support a wide spectrum of applications, including Artificial Intelligence, Virtual Reality, and Internet of Things (IoT). Thus, differentiating User Equipments (UEs) with different and varying Traffic-Profiles (TP) has become necessary due to each application's unique performance requirements. However, LEO satellites have limited onboard resources and the launched constellations ensure that each UE will be covered by more than one LEO satellite at any given moment, making it challenging to select the optimal satellite at any given time to assure the optimum QoS. Therefore, a satellite HO strategy has to effectively use the few available satellite resources and prevent network congestion while respecting the various resource requirements per TP. To address all the above requirements, we propose a user-centric Multi-Agent Deep Q-Network (MADQN) satellite HO strategy, that is the first in the state of the art to address the variety and diversity of UEs' performance requirements and generated traffic statistics. Our method showcases a significant achievement of approximately 60% reduction in HO rate and around 91% reduction in blocking rate compared to conventional single criterion approaches
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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