Cell- and Area-based ML Models: Unlocking High Precision Models for Radio Access Networks

• Published in: 2023 IEEE Wireless Communications and Networking Conference (WCNC) pp. 1 - 6
• Main Authors: Geuer, Philipp, Palaios, Alexandros, Zhohov, Roman
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2023
• Subjects: Artificial Intelligence; Data collection; Data models; Degradation; Heuristic algorithms; Machine Learning; Network architecture; Network Dynamics; Training; Wireless networks
• ISSN: 1558-2612
• DOI: 10.1109/WCNC55385.2023.10118824

Cellular networks evolve towards future generations, facing unprecedented levels of device and network programmability. At the same time, the new vision of the cyber-physical continuum will rely on diverse network architectures in extremely dense deployments. The emergence of new types of cells, like mobile and drone ones, would rely on instantly available AI/ML algorithms to provide a service within a few seconds after being powered on, avoiding long periods of data collection and training.In this work, we discuss how cell-specific characteristics, like the radio environment, can impact area-based ML models. Even though area-based models simplify the management of ML workflows considerably, there is also a need for cell-based models as these tend to provide better performance. Moreover, we show that area-based models can be part of ML workflow as they can complement cell-based ones. We finalize our work by discussing the possibility of reusing available ML models from other cells as a way of reducing the time needed for applying ML algorithms in newly deployed cells. We provide initial insights on the model re-usability and performance assessment and highlight the need for more research in this direction.In our work, we utilize the data from a test network, allowing us to explore the dynamics of real networks and provide results with increased confidence.