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.

翻译：现代无线应用需要能够捕捉下一代（NextG）蜂窝网络全部复杂性的测试环境。尽管数字孪生技术有望实现逼真的仿真，但现有解决方案往往在物理层保真度与可扩展性上有所妥协，或依赖于专用硬件。我们提出了Tiny-Twin，一个CPU原生的全栈数字孪生框架，能够在商用CPU上实现逼真、可重复的5G实验。Tiny-Twin将时变多抽头卷积与完整的5G协议栈集成，支持多种信道轨迹的即插即用式回放。通过重新设计的软件架构和系统级优化，Tiny-Twin完全在软件中实现了细粒度卷积。凭借内置的实时无线接入网智能控制器（RIC）集成和每用户设备（UE）信道隔离，它便于对网络算法和协议设计进行严格测试。我们的评估表明，Tiny-Twin能够扩展到多个并发UE，同时保持协议时序和端到端行为，在低保真度模拟器和高成本硬件仿真器之间提供了一个实用的折中方案。我们将Tiny-Twin作为开源平台发布，旨在为下一代蜂窝网络研究提供易于使用、高保真度的实验环境。