Calibration of the 5G-LENA System Level Simulator in 3GPP reference scenarios
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Abstract (arXiv)
Due to the rapid technology evolution and standardization activity in the mobile communication networks, there is the need for the research community to be able to develop, test and evaluate new and/or already xisting solutions before industrial or real-network implementation. As such, it is essential to have an open-source tool that provides an alternative solution to that of industrial proprietary simulators that are not available for public usage. ns-3 5G-LENA simulator is an end-to-end open-source NR system-level simulator that allows extensive research to be performed. However, it is of great importance to guarantee that the results obtained using the simulator can be comparable to that of industrial simulators and real networks. For this reason, calibrating the simulator based on 3GPP defined specifications is crucial. Based on the above, in this paper we calibrate the ns-3 5G-LENA simulator according to the 3GPP reference results for NR-based outdoor deployments. Moreover, we explore the REM feature provided by the simulator, to ease the calibration process and understand better the radio environment. Results show the resemblance of the simulator performance to that of simulators used as references by 3GPP.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 May 2022.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 0; 2026: 3.
5 papers.
- 2022A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameterscited by 2
- 2023Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approachcited by 2
- 2026Agentic-V2X: Small Language Model Agents for Deadline-Aware V2X Scheduling in 5G/6G Networkscited by 0
- 2026NS3Learn: Transferring 5G NR Mode-2 Reception Realism from ns-3 to the Veins/SUMO Stack for Connected-Vehicle Safety Assessmentcited by 0
- 2026Network-in-the-Loop at Scale: GPU-Batched 5G Simulation for Massively Parallel Robot Learningcited by 0
- 2026Network-in-the-Loop at Scale: GPU-Batched 5G Simulation for Massively Parallel Robot Learning1 Oct 2026
- 2026NS3Learn: Transferring 5G NR Mode-2 Reception Realism from ns-3 to the Veins/SUMO Stack for Connected-Vehicle Safety Assessment17 Sep 2026
- 2026Agentic-V2X: Small Language Model Agents for Deadline-Aware V2X Scheduling in 5G/6G Networks5 Jul 2026
- 2023Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach31 Dec 2023
- 2022A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters5 Nov 2022