CRRM: A 5G system-level simulator

2025-11-04

AI Summary

Existing 5G system-level simulators struggle to balance simulation speed and seamless integration with AI frameworks, hindering co-design research at the intersection of wireless communications and machine learning. To address this, we propose a novel computation-graph-based simulation paradigm that replaces traditional discrete-event simulation with a directed graph composed of differentiable computational blocks, enabling demand-driven, intelligent update strategies—thereby significantly improving both efficiency and flexibility. Implemented entirely in pure Python with a modular architecture, the simulator natively supports mainstream AI frameworks such as PyTorch and TensorFlow. Our open-source simulator preserves physical modeling fidelity while accelerating typical scenario simulations by one to two orders of magnitude. This substantially lowers the technical barrier for joint development of communication algorithms and deep learning models, providing an efficient, scalable, and AI-native system-level simulation infrastructure for 6G intelligent wireless networks.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Graph-based Machine Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

System-level simulation is indispensable for developing and testing novel algorithms for 5G and future wireless networks, yet a gap persists between the needs of the machine learning re- search community and the available tooling. To address this, we introduce the Cellular Radio Reference Model (CRRM), an open-source, pure Python simulator we designed specifically for speed, usability, and direct integration with modern AI frameworks. The core scientific contribution of CRRM lies in its architecture, which departs from traditional discrete-event simulation. We model the system as a set of inter-dependent computational blocks which form nodes in a directed graph. This enables a compute-on-demand mechanism we term smart update.

Problem

• Bridging the gap between machine learning needs and wireless network simulators
• Providing a fast Python simulator for 5G algorithm development
• Introducing smart update architecture for efficient system modeling

Innovation

• Open-source Python simulator for 5G networks
• Architecture uses interdependent computational blocks graph
• Smart update mechanism enables compute-on-demand system