Tiny-Twin: A CPU-Native Full-stack Digital Twin for NextG Cellular Networks

2026-01-13

AI Summary

Existing digital twin approaches for NextG cellular network emulation often sacrifice physical-layer fidelity or rely on specialized hardware, making it difficult to achieve high fidelity, scalability, and generality simultaneously. This work proposes the first fully CPU-based, full-stack digital twin framework that, for the first time, enables co-simulation of fine-grained, time-varying multi-tap convolution channels and a complete 5G protocol stack entirely in software. The framework supports per-user channel isolation, plug-and-play replay of channel trajectories, and real-time integration with RAN Intelligent Controllers (RICs). Without requiring dedicated hardware, it delivers high-fidelity, reproducible multi-user concurrent emulation while accurately preserving protocol timing and end-to-end behavior. By bridging the gap between low-fidelity simulators and costly hardware emulators, this solution offers a practical and accessible alternative, with source code publicly released.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Modern wireless applications demand testing environments that capture the full complexity of next-generation (NextG) cellular networks. While digital twins promise realistic emulation, existing solutions often compromise on physical-layer fidelity and scalability or depend on specialized hardware. We present Tiny-Twin, a CPU-Native, full-stack digital twin framework that enables realistic, repeatable 5G experimentation on commodity CPUs. Tiny-Twin integrates time-varying multi-tap convolution with a complete 5G protocol stack, supporting plug-and-play replay of diverse channel traces. Through a redesigned software architecture and system-level optimizations, Tiny-Twin supports fine-grained convolution entirely in software. With built-in real-time RIC integration and per User Equipment(UE) channel isolation, it facilitates rigorous testing of network algorithms and protocol designs. Our evaluation shows that Tiny-Twin scales to multiple concurrent UEs while preserving protocol timing and end-to-end behavior, delivering a practical middle ground between low-fidelity simulators and high-cost hardware emulators. We release Tiny-Twin as an open-source platform to enable accessible, high-fidelity experimentation for NextG cellular research.

Problem

• digital twin
• NextG cellular networks
• physical-layer fidelity
• scalability
• commodity hardware

Innovation

• CPU-native
• full-stack digital twin
• time-varying multi-tap convolution
• 5G protocol stack
• real-time RIC integration