Simulator for Mobile Networks (2024)

Simulator for Mobile Networks (1)

Der Simulator für Mobilfunknetze (SiMoNe) ist ein Software-Framework, mit dem Funknetze so realistisch wie möglich simuliert werden können. SiMoNe unterstützt u.a. die Funknetzplanungen und Simulation der gängigsten Mobilfunksysteme (GSM, LTE, 5G), die Simulation der Fahrzeugkommunikation (V2X) auf Grundlage des 802.11p Funkstandards und gerichtete Multi-Gigabit-Funkstrecken nach IEEE802.15.3d , die beispielsweise in drahtlosen Rechenzentren oder als Backhaul-Links verwendet werden. Das SiMoNe-Framework integriert dabei strahlenoptische Funkkanalprädiktionen (Raytracing), die Simulation der Bitübertragungsschickt einzelner Punkt-zu-Punkt-Verbindung (Link Level Simulator) sowie die Nachbildung kompletter Mobilfunksysteme unter Betrachtung tausender Basisstationen und Mobilfunknutzer (System-Level Simulator).

SiMoNe Video

Beitrag zum Tag der Informatik 2021

Funkkanalprädiktion

Innerhalb des SiMoNe-Frameworks kommen drei Kanalprädiktoren zum Einsatz, die im Rahmen aktueller Projekte stetig weiterentwickelt werden. Für die schnelle großflächige Prädiktion kommt der Makro-Prädiktor zum Einsatz. Hiermit sind Prädiktionen u.a. mit dem Okumura-Hata-Ausbreitungsmodell möglich. Der FemtoPred ist ein strahlenoptischer Prädiktor (Raytracer) mit dem Funkkanalprädiktionen unter Beachtung der 3D Gebäudedaten, Antennendiagrammen und Materialeigenschaften möglich sind. Für hoch dynamische Funknetze von bewegten Teilnehmern kommt der so genannte Mobility-Prädiktor (MobPred) zum Einsatz. Der MobPred ist ein hoch optimierter Raytracer mit dem strahlenoptische Pfadverlustprädiktionen für Ad-hoc-Netzwerke in Echtzeit möglich sind.

Link Level Simulator

Der Link Level Simulator in SiMoNe ermöglicht die detaillierte Simulation der Bitübertragungsschicht eines Kommunikationssystems. Dabei verfolgen wir einen universellen und vollständig parametrierten Ansatz, der eine große Zahl von Codier- und Modulationsverfahren, realistische Modelle von Mobilfunkkanälen bis hin zu Bauteileigenschaften im Sender und Empfänger berücksichtigt. Entstanden ist der Link Level Simulator im Rahmen der Forschungsprojekte TERAPOD, ThoR und meteracom. Er ist IEEE 802.15.3d konform und legt zurzeit einen Schwerpunkt auf die Simulation der Bitfehlerwahrscheinlichkeit von Einzelträgerverfahren für die THz Kommunikation. Signalformen und Simulationsergebnisse können auf vielfältige Art und Weise visualisiert werden, wie beispielweise in Augendiagrammen, I-/Q-Diagrammen oder Leistungsspektren.

System Level Simulator

SiMoNe unterstützt System-Level Simulationen von unbegrenzt vielen Nutzern und Basisstationen entlang eines festen Zeitraster. Üblich ist hier eine Zeitauflösung von 100 ms. Damit ist die realistische zeitvariante Nachbildung von kompletten Mobilfunknetzen möglich. Für die Nutzerbewegungen stehen diverse Mobilitätsmodelle zur Auswahl, u.a. Fußgänger, Autos, Züge oder Drohnen. Der Datenverkehr zwischen Nutzern und Mobilfunkstationen wird dabei stochastisch modelliert auf Basis diverse Datenmodelle. Als Nutzeranwendungen stehen u.a. Telefonie, Videostream oder FTP-Datenverkehr zur Auswahl. Die Mobilfunkverbindung inkl. Zellwechsel wird entsprechend des gewünschten Funkstandards nachgebildet (GSM, LTE, 5G) wobei der Funkkanal strahlenoptisch prädiziert wird.

Seit dem 01.11.2021 verweisen wissenschaftliche Veröffentlichungen auf die verwendete SiMoNe Softwareversion. Versionsänderungen können den folgenden Changelogs entnommen werden:

SiMoNe Release Notes

Release v2021.01, 25.09.2021

Zum Changelog

Release v2022.01, 21.02.2022

Zum Changelog

Release v2022.02, 10.06.2022

Zum Changelog

Release v2023.01, 19.10.2023

Zum Changelog

Kontakt

ifn-simone(at)tu-braunschweig.de

Weiterführende Veröffentlichungen

M. Schweins, L. Thielecke, N. Grupe and T. Kürner, "Optimization and Evaluation of a 3-D Ray Tracing Channel Predictor Individually for Each Propagation Effect," in IEEE OJAP, 2024. https://doi.org/10.1109/OJAP.2024.3366070

J. M. Eckhardt, THz Communications in a Data Center: Channel Measurements, Modeling and Physical Layer Analysis (Mitteilungen aus dem Institut für Nachrichtentechnik der Technischen Univeristät Braunschweig 81). Düren: Shaker, Jun. 2024. https://doi.org/10.24355/dbbs.084-202405061045-0

N. Grupe, Realistic Simulation for Vehicular Communication Scenarios using a Deterministic Channel Model (Mitteilungen aus dem Institut für Nachrichtentechnik der Technischen Univeristät Braunschweig 74). Düren: Shaker, Jun. 2023.

J. M. Eckhardt, C. Herold, T. Kürner,"Intercarrier Interference at Terahertz Frequencies for IEEE Std 802.15.3d Multiband Transmissions" in Proc. 26th Int. ITG Workshop Smart Antennas 13th Conf. Syst. Commun. Coding, 2023, pp. 173-178.

Eckhardt, J. M., Herold, C., Jung, B. K., Dreyer, N., & Kürner, T. (2022). Modular Link Level Simulator for the Physical Layer of Beyond 5G Wireless Communication Systems. Radio Science, 57, e2021RS007395. https://doi.org/10.1029/2021RS007395

Eckhardt, J.M., Herold, C., Jung, B.K., Dreyer, N., Kuerner, T., 2022. Link Level Simulations of the Physical Layer for Low THz Communication Systems: Validation and Analysis of a Data Center Use Case. https://doi.org/10.24355/dbbs.084-202111032122-0

D. M. Rose, Realistic Network-Level Simulations for Cellular Radio Networks: Models, Propagation, Software, and Case Studies (Mitteilungen aus dem Institut für Nachrichtentechnik der Technischen Univeristät Braunschweig 71). Düren: Shaker, Nov. 2022.

J. M. Eckhardt, C. Herold, B. Friebel, N. Dreyer and T. Kürner, "Realistic Interference Simulations in a Data Center Offering Wireless Communication at Low Terahertz Frequencies," 2021 International Symposium on Antennas and Propagation (ISAP), 2021, pp. 1-2, doi: 10.23919/ISAP47258.2021.9614511.

T. Doeker and T. Kürner, "Influence of the Initial Antenna Orientation on the Performance of Compressed Sensing-assisted Device Discovery," 2021 Kleinheubach Conference, 2021, pp. 1-4, doi: 10.23919/IEEECONF54431.2021.9598425.

T. Doeker, P. Reddy Samala, P. S. Negi, A. Rajwade and T. Kürner, "Angle of Arrival and Angle of Departure Estimation Using Compressed Sensing for Terahertz Communications," 2021 15th European Conference on Antennas and Propagation (EuCAP), 2021, pp. 1-5, doi: 10.23919/EuCAP51087.2021.9411406.

B. K. Jung, C. Herold, J. M. Eckhardt and T. Kürner, "Link-Level and System-Level Simulation of 300 GHz wireless Backhaul Links," 2020 International Symposium on Antennas and Propagation (ISAP), 2021, pp. 619-620, doi: 10.23919/ISAP47053.2021.9391508.

T. Kürner and B. K. Jung, "Automatic Planning of NLOS Backhaul Links at 300 GHz arranged in Star Topology," 2021 XXXIVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), 2021, pp. 1-3, doi: 10.23919/URSIGASS51995.2021.9560305.

N. Dreyer and T. Kürner, "Comparison of a Fast Analytical Ray Tracer and Channel-Sounder Measurements for V2V Communications," 2020 14th European Conference on Antennas and Propagation (EuCAP), 2020, pp. 1-5, doi: 10.23919/EuCAP48036.2020.9135856.

N. Dreyer and T. Kürner, "A Comparison of Stochastic and Deterministic Channel Models for V2V Applications," 2020 European Conference on Networks and Communications (EuCNC), 2020, pp. 79-83, doi: 10.1109/EuCNC48522.2020.9200903.

B. K. Jung, N. Dreyer, J. M. Eckhardt and T. Kürner, "Simulation and Automatic Planning of 300 GHz Backhaul Links," 2019 44th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz), 2019, pp. 1-3, doi: 10.1109/IRMMW-THz.2019.8873734.

T. Nan, G. Jornod, M. Schweins, A. E. Assaad, A. Kwoczek and T. Kürner, "Channel Models for the Simulation of Different RATs Applied to Platoon Emergency Braking," 2019 European Conference on Networks and Communications (EuCNC), 2019, pp. 193-197, doi: 10.1109/EuCNC.2019.8801963.

N. Dreyer, G. Artner, M. Hein, F. Backwinkel and T. Kürner, "Evaluating Automotive Antennas for Cellular Radio Communications," 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), 2019, pp. 1-5, doi: 10.1109/ICCVE45908.2019.8965240.

N. Dreyer and T. Kürner, "An Analytical Raytracer for Efficient D2D Path Loss Predictions," 2019 13th European Conference on Antennas and Propagation (EuCAP), 2019, pp. 1-5.

S. Hahn, M. Schweins and T. Kürner, "Impact of SON function combinations on the KPI behaviour in realistic mobile network scenarios," 2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), 2018, pp. 1-6, doi: 10.1109/WCNCW.2018.8368974.

G. Jornod, T. Nan, M. Schweins, A. E. Assaad, A. Kwoczek and T. Kürner, "Sidelink Technologies Comparison for Highway High-Density Platoon Emergency Braking," 2018 16th International Conference on Intelligent Transportation Systems Telecommunications (ITST), 2018, pp. 1-7, doi: 10.1109/ITST.2018.8566954.

N. Dreyer, A. Moeller, J. Baumgarten, Z. H. Mir, T. Kuerner and F. Filali, "On Building Realistic Reference Scenarios for IEEE 802.11p/LTE-Based Vehicular Network Evaluations," 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), 2018, pp. 1-7, doi: 10.1109/VTCSpring.2018.8417786.

L. Richter, N. Dreyer, S. Ilsen, F. Juretzek and D. Rother, "System-level simulation of a multilayer broadcast and broadband system," 2017 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), 2017, pp. 1-9, doi: 10.1109/BMSB.2017.7986170.

N. Dreyer, A. Moller, Z. H. Mir, F. Filali and T. Kurner, "A Data Traffic Steering Algorithm for IEEE 802.11p/LTE Hybrid Vehicular Networks," 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), 2016, pp. 1-6, doi: 10.1109/VTCFall.2016.7880850.

D. M. Rose, S. Hahn and T. Kürner, "Evolution from network planning to SON management using the simulator for mobile networks (SiMoNe)," 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2016, pp. 1-2, doi: 10.1109/PIMRC.2016.7794575.

S. Hahn, D. M. Rose, C. Herold and T. Kurner, "Impact of Correlated Group Mobility Modelling in the Context of Realistic Mobile Network Simulation Scenarios," 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), 2016, pp. 1-5, doi: 10.1109/VTCFall.2016.7881037.

D. M. Rose, T. Jansen, T. Werthmann, U. Turke and T. Kurner, "The Urban Hannover Scenario - Realistic 3D Pathloss Predictions and Mobility Patterns," 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), 2016, pp. 1-7, doi: 10.1109/VTCFall.2016.7881253.

S. Hahn, D. M. Rose, J. Sulak and T. Kurner, "Impact of Realistic Pedestrian Mobility Modelling in the Context of Mobile Network Simulation Scenarios," 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), 2015, pp. 1-5, doi: 10.1109/VTCSpring.2015.7145870.

D. M. Rose, J. Baumgarten, S. Hahn and T. Kurner, "SiMoNe - Simulator for Mobile Networks: System-Level Simulations in the Context of Realistic Scenarios," 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), 2015, pp. 1-7, doi: 10.1109/VTCSpring.2015.7146084.

J. Nuckelt, M. Schack and T. Kürner, "Geometry-based path interpolation for rapid ray-optical modeling of vehicular channels," 2015 9th European Conference on Antennas and Propagation (EuCAP), 2015, pp. 1-5.

J. Nuckelt, T. Abbas, F. Tufvesson, C. Mecklenbrauker, L. Bernado and T. Kurner, "Comparison of Ray Tracing and Channel-Sounder Measurements for Vehicular Communications," 2013 IEEE 77th Vehicular Technology Conference (VTC Spring), 2013, pp. 1-5, doi: 10.1109/VTCSpring.2013.6692484.

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Simulator for Mobile Networks (2024)

FAQs

How to simulate wireless network? ›

Interaction Between Network Simulator and Wireless Nodes
  1. Run the protocol stack of nodes.
  2. Obtain the generated packet from the transmit buffer of a node.
  3. Check the relevance of the packet to other nodes.
  4. Apply channel impairments to the obtained packet and distribute it the interested nodes in the network.

What is a network simulator tool? ›

A network simulator is a software program that can predict the performance of a computer network or a wireless communication network. Since communication networks have become too complex for traditional analytical methods to provide an accurate understanding of system behavior, network simulators are used.

What is simulator in 5G? ›

Simu5G is the evolution of the popular SimuLTE 4G network simulator that incorporates 5G New Radio access. Based on the OMNeT++ framework, it is written in C++ and is fully customizable with a simple pluggable interface. One can also develop new modules implementing new algorithms and protocols.

What is the difference between a network simulator and an emulator? ›

Network simulators create models, while emulators mimic networks. Network engineers can't -- and shouldn't -- always work directly on the production network. To prepare for network changes, network engineers need to test how those changes can affect network function.

What is a wireless simulator? ›

The wirelessNetworkSimulator object simulates different wireless network scenarios with different types of wireless nodes. Use the Object Functions to add nodes to the simulator, interact with the nodes, schedule actions to perform during simulation, plug in custom channel models, and run simulations.

What is Wi-Fi simulation? ›

Simulation is used to predict the performance of a wireless network's architecture, protocol, device, topology, etc. It is a cost-effective and flexible technique to performance evaluation of wireless systems.

How do you simulate network outages? ›

To simulate packet loss, you can use an open source tool called tc (traffic control). tc can be installed on any Linux host. With this tool you can introduce packet loss to a network interface, as well as increase in latency and round-trip time.

Who is the best SIM network? ›

Reliance Jio is widely regarded as the best SIM network in India due to its extensive coverage, affordable plans, high-speed 4G LTE services, and strong data network. Jio is the outright winner of both the Download Speed Experience and 5G Download Speed awards, with scores of 22.5Mbps and 315.3Mbps, respectively.

Which is better, GNS3 or Eve-Ng? ›

EVE-NG: Although it has a growing community and good professional support, it's slightly less widespread than GNS3's community. GNS3: Boasts a large, active community with extensive resources, tutorials, and forums available for troubleshooting and learning, which can be a significant advantage for newcomers.

Which is better, GNS3 or Packet Tracer? ›

Ease of Use: Cisco Packet Tracer is the easiest to use, followed by EVE-NG and GNS3, while VIRL and eNSP have a steeper learning curve. Programmability: GNS3, EVE-NG, VIRL, and eNSP all support network automation and programmability through Python scripting, while Cisco Packet Tracer does not.

Which is the best SIM network for gaming? ›

Our regional analysis of Mobile Network Experience across 22 Indian telecom circles reveals that Airtel is top for gaming, video streaming, and voice app experiences in most number of circles, while Jio excels in download speeds, Availability and 5G Availability and coverage metrics.

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