# New Radio Physical Layer Abstraction for System-Level Simulations of 5G Networks

arXiv: https://arxiv.org/abs/2001.10309 · HTML: https://mlsanity.com/paper/2001.10309/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 7 of 7)

- [A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters](https://mlsanity.com/paper/2211.09706/) (2022)
- [Bit-Metric Decoding Rate in Multi-User MIMO Systems: Theory](https://mlsanity.com/paper/2203.06271/) (2022)
- [LOLLA: Deep Reinforcement Learning for Closed-Loop Link Adaptation Towards a GPU-Accelerated AI-RAN](https://mlsanity.com/paper/2606.23110/) (2026)
- [Bit-Metric Decoding Rate in Multi-User MIMO Systems: Applications](https://mlsanity.com/paper/2203.06273/) (2022)
- [Robust Nonlinear Transform Coding: A Framework for Generalizable Joint Source-Channel Coding](https://mlsanity.com/paper/2511.18884/) (2025)
- [NS3Learn: Transferring 5G NR Mode-2 Reception Realism from ns-3 to the Veins/SUMO Stack for Connected-Vehicle Safety Assessment](https://mlsanity.com/paper/2609.20578/) (2026)
- [Network-in-the-Loop at Scale: GPU-Batched 5G Simulation for Massively Parallel Robot Learning](https://mlsanity.com/paper/2610.02370/) (2026)

## Abstract (arXiv)

A physical layer (PHY) abstraction model estimates the PHY performance in system-level simulators to speed up the simulations. This paper presents a PHY abstraction model for 5G New Radio (NR) and its integration into an open-source ns-3 based NR system-level simulator. The model capitalizes on the exponential effective signal-to-interference-plus-noise ratio (SINR) mapping (EESM) and considers the latest NR specification. To generate it, we used an NR-compliant link-level simulator to calibrate the EESM method as well as to obtain SINR-block error rate (BLER) lookup tables for various NR configurations. We also illustrate the usability of the developed model through end-to-end simulations in ns-3, under different NR settings of modulation and coding schemes, hybrid automatic repeat request combining methods, and link adaptation approaches.