CellTwin: Reproducible Cellular Network Emulation via High Resolution, in situ Measurement-Driven ML
CellTwin is a high-fidelity emulation framework that uses machine learning to recreate complex 5G scheduler behaviors directly from real-world network telemetry. It addresses the gap between theoretical algorithm design and practical cellular performance by learning resource allocation and modulation schemes dynamically, enabling accurate testing of real-time network protocols.