Abstract: A device may receive real mobile radio data identifying measurements of radio transmissions of base stations and user devices of a mobile radio environment in a geographical area, and may receive network topology data associated with the geographical area. The device may utilize, based on the network topology data, a machine learning feature extraction approach to generate a representation of invariant aspects of spatiotemporal predictable components of the real mobile radio data, and may generate, based on the representation of invariant aspects, stochastic data that includes a probability that a radio signal will be obstructed. The device may utilize the stochastic data to identify a realistic discoverable spatiotemporal signature, and may train or evaluate a system to manage performance of a mobile radio network based on the realistic discoverable spatiotemporal signature.

Abstract: A device may receive mobile radio data identifying utilization of a mobile radio network that includes base stations and user devices in a geographical area. The device may process the mobile radio data, with a machine learning feature extraction model, to generate a behavioral representation, that is probabilistic in nature, of invariant aspects of spatiotemporal utilization of the mobile radio network. The device may generate one or more instances of the spatiotemporal utilization of the mobile radio network that reflects the probabilistic nature of a spatiotemporal predictable component of the behavioral representation. The device may utilize the one or more instances of the spatiotemporal utilization of the mobile radio network as a dataset for training or evaluating a system to manage performance of the mobile radio network.

Abstract: A device may receive a first type of data identifying measurements associated with user devices and/or base stations of a mobile radio environment, and a second type of data identifying spatiotemporal behavior associated with the user devices. The device may train a first model, with the first type of data, to generate a trained first model that yields dimensionality-reduced spatiotemporal characteristics of the first type of data, and may train a second model, with the second type of data and the dimensionality-reduced spatiotemporal characteristics, to generate a trained second model. The device may receive particular data identifying measurements associated with a user device and/or base stations, and may process the particular data, with the trained first model, to generate a dimensionality-reduced spatiotemporal characteristic of the particular data.