Description
Synthetic data generators (SDGs) are indispensable tools in telecommunications and satellite communications, providing a means to simulate otherwise hard-to-obtain realistic traffic scenarios for pre-deployment testing and system optimization. This paper introduces a framework for generating synthetic bandwidth demand data by integrating macro-scale and micro-scale approaches. The macro-scale SDG models long-term trends, daily and weekly seasonality, random noise, and occasional spikes over an extended period of time, typically a year. In contrast, the micro-scale SDG captures short-term, minute-level variations within a day or week, adjusted for different application demands such as phone calls, video calls, and video streams. The proposed ensemble SDG merges these scales, producing synthetic datasets that provide high fidelity in both broad and granular temporal views of bandwidth demand. We further extend the model by scaling demand with population density and projecting it onto satellite beam footprints for SatCom applications. This paper details the mathematical formulations, implementations, and theoretical underpinnings of each SDG component, demonstrating their effectiveness and realism through experimentation. The proposed framework supports a wide range of applications, enhancing the ability to plan, optimize, and innovate in the field of (not only satellite) telecommunications.
