Telefonaktiebolaget Lm Ericsson (Publ)

Abhishek Sarkar

Kaushik Dey

Ashis Kumar Roy

Konstantinos Vandikas

Christofer Flinta

Lackis Eleftheriadis

Selim Ickin

Valentin Kulyk

Dhiraj Nagaraja Hegde

Leonid Mokrushin

Daniel Wilson

The disclosure provides methods, apparatus and machine-readable mediums for mitigating power failures in base stations in wireless communication networks. In one aspect of the disclosure, a method for mitigating power failures in a wireless communication network is provided. The method comprises, responsive to a determination that a primary power supply for a base station in the wireless communication network has failed or is predicted to fail, initiating deployment of an unmanned aerial vehicle to the base station to charge one or more batteries providing a secondary power supply for the base station.

Methods, apparatus and machine-readable mediums for mitigating power failures in a communication network

A method manages a group of Unmanned Aerial Vehicles (UAVs) operable to fly in a formation. Each UAV is programmed with a task to be performed by the UAV. The method is performed in a controller UAV of the group and comprises receiving UAV status information from UAVs in the group, obtaining information on a current formation of the group and combining the received UAV status information with the information on current group formation to form a representation of a first state of the group. A machine learning model is used to predict, based on the first state of the group, an optimal formation transition to a new formation and to instruct the UAVs in the group to perform the predicted optimal formation transition, which is a transition to a formation that will minimise predicted total energy consumption for all UAVs in the group to complete their tasks.

Formation flight of unmanned aerial vehicles

It is provided a method for adjusting alignment for microwave transmissions from a microwave transmitter to a microwave receiver based on a reinforcement learning, RL, model. The method comprises the steps of: obtaining state space comprising external state space and internal state space, the external state space comprising at least one value of a parameter related to environmental conditions, and the internal state space relates to alignment of the microwave transmitter; determining an action in an action space, the action space comprising actions to adjust alignment of the microwave transmitter; obtaining a measurement of path loss for a transmission from the microwave transmitter to the microwave receiver; determining a reward value based on the path loss, wherein an increase in path loss results in a reduced reward value; and adjusting the RL model based on the obtained state space, the determined action and the determined reward value.

Adjusting alignment for microwave transmissions based on an RL model

Managing network event data in a telecommunication network. The method includes obtaining a plurality of datasets having network event data associated with products provided by a different vendor, the network event data comprising a plurality of data instances representing a plurality of network event features. The method further comprises obtaining metadata describing the network event data in the datasets and generating feature description vectors from the obtained metadata, calculating a metric of the relation between network event features represented in the datasets and constructing a graph of the network event features represented in the datasets, with edges weighted according to the calculated metric, partitioning the graph into clusters by minimising an edge cut between network event features, labelling network event features in the cluster with a normalised network event feature index and inputting the labelled network event data to a model for making at least one of recommendations or predictions.

Managing telecommunication network event data

Methods and devices optimize backup power management for network devices connected to backup power sources including two different batteries. Machine learning techniques are used to predict upcoming power outages affecting the network device based on power-related historic information and current conditions. A backup-power operation plan prescribing usage of the backup power sources during the predicted power outages is then generated. The backup-power operation plan schedules a battery among the backup power devices to be used at least twice without being recharged.

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Bengaluru, India

Abhishek Sarkar