Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network entity may calculate, using a machine learning (ML) model trained to estimate an impact of a reconfiguration of an antenna on a set of key performance indicators (KPIs) of a given cell and one or more neighbors of the given cell, one or more predicted KPIs using data characterizing a reconfiguration of a massive multiple-input multiple-output (M-MIMO) antenna. The network entity may provide the one or more predicted KPIs. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a network node may obtain a machine learning (ML) model trained to provide one or more predicted channel interference (CI) indicators informative of channel interference between cells of a cellular network. The network node may calculate, using the ML model, the one or more predicted CI indicators using data characterizing a given cell and one or more neighbor cells of the given cell. The network node may provide the one or more predicted CI indicators. Numerous other aspects are described.

Abstract: There are provided a method and system to control traffic in a cellular network comprising a plurality of access points (APs) serving a plurality of user equipment devices (UEs). The method comprises: using a machine learning (ML) model to classify at least part of the APs in accordance with a first part of AP metrics thereof, thereby giving rise to a plurality of classes, each comprising peering APs; for a given class, processing AP performance metrics of peering APs classified to the given class to identify, among them, one or more first APs with negative performance variations above a variation threshold and, thereby, requiring corrective actions; and enabling one or more corrective actions with regard to the identified one or more first APs.

Abstract: A method including receiving at least one image stream depicting a scene, processing the image stream to detect multiple parameters associated with at least (i) one or more persons in the scene, (ii) one or more objects in the scene, and (iii) one or more environmental conditions in the scene, where each of the parameters has an associated score, calculating an alert value with respect to the scene based on a weighted sum of the scores, and issuing an alert when the alert value is above a predefined threshold.