End-to-End Simulation of 5G mmWave Networks
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Abstract (arXiv)
Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation cellular systems. While there has been considerable progress in understanding the mmWave physical layer, innovations will be required at all layers of the protocol stack, in both the access and the core network. Discrete-event network simulation is essential for end-to-end, cross-layer research and development. This paper provides a tutorial on a recently developed full-stack mmWave module integrated into the widely used open-source ns--3 simulator. The module includes a number of detailed statistical channel models as well as the ability to incorporate real measurements or ray-tracing data. The Physical (PHY) and Medium Access Control (MAC) layers are modular and highly customizable, making it easy to integrate algorithms or compare Orthogonal Frequency Division Multiplexing (OFDM) numerologies, for example. The module is interfaced with the core network of the ns--3 Long Term Evolution (LTE) module for full-stack simulations of end-to-end connectivity, and advanced architectural features, such as dual-connectivity, are also available. To facilitate the understanding of the module, and verify its correct functioning, we provide several examples that show the performance of the custom mmWave stack as well as custom congestion control algorithms designed specifically for efficient utilization of the mmWave channel.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 May 2017.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 3; 2024: 1; 2025: 2.
10 papers.
- 2022Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architecturescited by 11
- 2022A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenariocited by 4
- 2025MITO: A Millimeter-Wave Dataset and Simulator for Non-Line-of-Sight Perceptioncited by 4
- 2021Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Fastercited by 3
- 2022Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3cited by 3
- 2023Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networkscited by 2
- 2023Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approachcited by 2
- 2023Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivitycited by 0
- 2024Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networkscited by 0
- 2025PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligencecited by 0
- 2025PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence15 Jul 2025
- 2025MITO: A Millimeter-Wave Dataset and Simulator for Non-Line-of-Sight Perception14 Feb 2025
- 2024Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks25 Apr 2024
- 2023Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach31 Dec 2023
- 2023Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks22 Feb 2023
- 2023Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity13 Jan 2023
- 2022Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures28 Sep 2022
- 2022Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-310 Mar 2022
- 2022A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario4 Feb 2022
- 2021Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster25 Dec 2021