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

## TL;DR

This paper introduces a comprehensive end-to-end simulation module for 5G mmWave networks within the ns-3 simulator, enabling detailed, customizable, and realistic modeling of physical, MAC, and network layers for research and development.

## Contribution

It presents a modular, customizable full-stack mmWave simulation module integrated with ns-3, including detailed channel models and support for advanced features like dual-connectivity.

## Key findings

- Demonstrated the module's ability to simulate realistic mmWave network performance.
- Showcased integration with LTE core network for end-to-end simulations.
- Validated custom congestion control algorithms for mmWave channels.

## Abstract

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.