Sumit Soman

Sumit Soman

Ph.D. (IIT Delhi) - Machine Learning, Senior Data Scientist, Ericsson R&D

Patents

First node, second node and methods performed thereby, for planning radio coverage in a space

July 18, 2024

WO2024150240A1, PCT/IN2023/050027, Telefonaktiebolaget Lm Ericsson (Publ)

Abstract: A computer-implemented method performed by a first node (111). The methods is for planning radio coverage in a space (170). The first node (111) operates in a communications system (100). The first node (111) determines (204), using machine learning (ML) and first radio coverage data from one or more first communications networks (120), an ML model. The ML model is to estimate a number of one or more radio antennas (142) necessary to provide radio coverage to the space (170). The estimate is to be performed in the absence of a floor plan corresponding to the space (170). The first node (111) also provides (208) an indication of the determined ML model to a second node (112) operating in the computer system (100).

Identifying sequences of machine learning models

July 11, 2024

WO2024147144A1, PCT/IN2023/050008, Telefonaktiebolaget Lm Ericsson (Publ)

Abstract: A computer-implemented method of identifying sequences of machine learning (ML) models of a plurality of ML models during a training phase is provided. The method comprises generating (301) one or more sequences of the ML models The method further comprises determining (302) a performance of each sequence of the ML models. Furthermore, the method comprises identifying (303) a sequence with performance greater than a threshold performance.

First node and methods performed thereby for handling location of a network node in a geographical area for operation in a communications system

October 12, 2022

WO2024079737A1, PCT/IN2022/050911, Telefonaktiebolaget Lm Ericsson (Publ)

Abstract: A computer-implemented method, performed by a first node, for handling location of a network node in a geographical area for operation in a communications system. The first node obtains first data indicating images of the geographical area over a first time period. The first node also obtains second data indicating data samples of performance indicators of radio communications, during the first time period, of devices in the geographical area. The first node determines, by performing a spatio-temporal correlation of the obtained first data and the obtained second data, one or more locations as candidates to place the network node for operation in the communications system. The determining is performed using machine learning or deep learning, and. The first node then outputs an indication of the determined one or more locations.

First node, second node, third node and methods performed thereby for handling predictive models

March 31, 2022

WO2023187793A1, PCT/IN2022/050323, Telefonaktiebolaget Lm Ericsson (Publ)

Abstract: A computer-implemented method, performed by a first node, for handling predictive models. The first node updates, using machine learning, a first predictive model of an indicator of performance of the communications system. The updating is based on respective explainability values respectively obtained from a first subset of a plurality of second nodes. The respective explainability values correspond to a first subset of respective second predictive models of the indicator of performance of the communications system (100), respectively determined by the first subset of the plurality of second nodes. The models in the first subset of respective second predictive models have a respective performance value above a threshold. The first node then provides an indication of the updated first predictive model to a third node comprised in the plurality of second nodes and excluded from the first subset, or to another node.

Node and methods performed thereby for handling drift in data

December 24, 2021

WO2023119304A1, PCT/IN2021/051204, Telefonaktiebolaget Lm Ericsson (Publ)

Abstract: A method performed by a node for handling drift in data. The node obtains a dataset comprising a plurality of datapoints corresponding to a plurality of values of one or more dependent variables for a plurality of first features over a time period. The node determines, using machine learning and explainability, in the absence of determining whether or not the plurality of datapoints has a drift, whether or not there has been a change in respective one or more characteristics of a subset of the plurality of first features having a largest contribution to a variability of the datapoints in the plurality of datapoints based on a threshold from a first time period to a second time period. The node then initiates application of a drift policy on the plurality of datapoints based on a result of the determination.

Optimizing user equipment service level agreement violations for network slice allocation

December 02, 2021

WO2024075130A1, PCT/IN2022/050899, Telefonaktiebolaget Lm Ericsson (Publ)

Abstract: A method performed in a network node includes performing drift detection in a slice to identify a number of data points in drift in at least one of network specific performance parameters and/or user equipment, UE, specific performance parameters. The method includes obtaining weighting parameters of the network and UE specific performance parameters. The method includes combining a function of data points in drift of the network and UE specific performance parameters with each data point of the number of data points in drift weighed by the weighting parameters associated with data point. The method includes determining one or more service level agreement, SLA, violations as a weighted average of individual drift in one or more of the at least one of network specific performance parameters and UE specific performance parameters. The method includes performing an action based on determining the one or more SLA violations.

First node, second node and methods performed thereby for handling data augmentation

December 02, 2021

WO2023100190A1, PCT/IN2021/051130, Telefonaktiebolaget Lm Ericsson (Publ)

Abstract: A method performed by a first node for handling data augmentation. The first node divides each epoch in an original dataset having an input space, into a set of batches. The first node generates a set of subsets of samples by selecting, within each batch from every set of batches, a respective plurality of subsets. The first node determines, using machine learning, a fourth set of clusters of data using the third set. The first node selects a fifth set of clusters from the fourth set based on a relevance criterion. The first node generates samples in each cluster of the fifth set, and refrains from generating samples in clusters of the fourth set excluded from the fifth set. The first node then generates a sixth set of augmented samples in the input space of the original dataset, by using the generated samples and applying a reverse projection approach.