# End-to-End Simulation of 5G mmWave Networks

arXiv: https://arxiv.org/abs/1705.02882 · HTML: https://mlsanity.com/paper/1705.02882/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2017.

## Built on by (most cited first, 10 of 10)

- [Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures](https://mlsanity.com/paper/2209.14171/) (2022)
- [A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario](https://mlsanity.com/paper/2202.01949/) (2022)
- [MITO: A Millimeter-Wave Dataset and Simulator for Non-Line-of-Sight Perception](https://mlsanity.com/paper/2502.10259/) (2025)
- [Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster](https://mlsanity.com/paper/2112.13194/) (2021)
- [Artificial Intelligence in Vehicular Wireless Networks: A Case Study Using ns-3](https://mlsanity.com/paper/2203.05449/) (2022)
- [Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks](https://mlsanity.com/paper/2302.11268/) (2023)
- [Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach](https://mlsanity.com/paper/2401.00443/) (2023)
- [Hierarchical Deep Q-Learning Based Handover in Wireless Networks with Dual Connectivity](https://mlsanity.com/paper/2301.05391/) (2023)
- [Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks](https://mlsanity.com/paper/2404.16920/) (2024)
- [PRATA: A Framework to Enable Predictive QoS in Vehicular Networks via Artificial Intelligence](https://mlsanity.com/paper/2507.14211/) (2025)

## 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.