2025/04/01 by Mohamed Benzaghta, Benzaghta, Mohamed, Giovanni Geraci +5
Engineering · #Advanced Data and IoT Technologies #Advanced MIMO Systems Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Millimeter-Wave Propagation and Modeling #Networking and Internet Architecture (cs.NI) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2504.00825
openalex publication_date 2025/04/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a data-driven approach for large-scale cellular network optimization, using a production cellular network in London as a case study and employing Sionna ray tracing for site-specific channel propagation modeling. We optimize base station antenna tilts and half-power beamwidths, resulting in more than double the 10%-worst user rates compared to a 3GPP baseline. In scenarios involving aerial users, we identify configurations that increase their median rates fivefold without compromising ground user performance. We further demonstrate the efficacy of model generalization through transfer learning, leveraging available data from a scenario source to predict the optimal solution for a scenario target within a similar number of iterations, without requiring a new initial dataset, and with a negligible performance loss.