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

TL;DR

Tiny-Twin is an open-source, CPU-native digital twin framework that enables realistic 5G network experimentation on commodity hardware, balancing fidelity, scalability, and cost.

Contribution

It introduces a full-stack, software-only digital twin with integrated channel modeling and protocol support, improving accessibility and realism for NextG research.

Findings

Supports multiple concurrent UEs with preserved protocol timing.

Achieves high-fidelity channel emulation entirely in software.

Scales to realistic 5G network scenarios on commodity CPUs.

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.