cool Simu5G logo

5G New Radio User Plane Simulation Model for INET & OMNeT++

Download this project as a .zip file Download this project as a tar.gz file

What is Simu5G: simulator for 5G New Radio networks

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.

Using Simu5G

The idea behind Simu5G is to let researchers simulate and benchmark their solutions on an easy-to-use framework. It borrows the concept of modularity from OMNeT++ thus it is easy to extend. Moreover it can be integrated with other modules from the INET Framework. It offers support to optimization tools (e.g. optimization solvers such as CPLEX).
Simu5G is compatible with SimuLTE, and allows one to simulate network scenarios where 4G and 5G coexist, in both StandAlone (SA) and E-UTRA/NR Dual Connectivity (ENDC) deployments. Furthermore, it inherits SimuLTE's compatiblity with other OMNeT++-based libraries, for instance Veins for vehicular mobility.

System Requirements

Simu5G can be used on any system compatible with OMNeT++ (Windows, Linux, or Mac OS X). See OMNeT++ page for more info.
Simu5G requires:
  • OMNeT++ v5.6.2
  • INET-Framework v4.2.2
For more information and installation instructions, see the Install Guide.

Main Features

User Terminals:
Mobility; Interference; all types of traffic; handover; network-assisted D2D communications
Macro, micro, pico gNBs; SA and ENDC deployments; Carrier Aggregation; TDD/FDD with numerologies; support for handover; Scheduling algorithms: Max C/I, Proportional Fair, Round Robin, etc.
Network emulation:
Simu5G can also run as a network emulator, integrating an emulated 5G network with real networks and applications.
UM and AM; segmentation and reassembly; retransmissions (AM only).
Buffering; PDU concatenation; CQI reporting and reception; transport format selection and resource allocation; Coding designed to facilitate cross-layer analysis.
H-ARQ; channel feedback computation; realistic channel models associated to CCs; correct reception based on SINR and BLER curves.

Core contributors and funding