Patents by Inventor Marin Orlic
Marin Orlic has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240430738Abstract: Embodiments herein disclose, e.g., a method performed by a network node, in a wireless communications network, for charging a rechargeable power source in the network node. The network node obtains an operational parameter to an operation of the network node, wherein the operational parameter is based on an output of a computational model. The computational model is based on a state of charge of the rechargeable power source, a parameter related to outage of a power grid, and a QoS parameter relating to radio communication in the wireless communications network. The network node further applies, during a charging of the rechargeable power source, the operational parameter to the operation of the network node.Type: ApplicationFiled: September 17, 2021Publication date: December 26, 2024Inventors: Lackis ELEFTHERIADIS, Athanasios KARAPANTELAKIS, Maxim TESLENKO, Aneta VULGARAKIS FELJAN, Marin ORLIC
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Publication number: 20240413425Abstract: Embodiments disclosed herein relate to methods and apparatus for managing heat transfer to a battery. In one embodiment there is provided a method for managing heat transfer to a battery (105) electrically coupled to an electronic device. The method comprises determining a battery temperature of the battery (210) and a predicted heat source parameter associated with the electronic device (215). A battery heat transfer action (220) in a thermal medium (120b, 320hc) coupled between the battery and the electronic device is performed dependent on the predicted heat source parameter (453h) and a difference between a prescribed wanted battery temperature (453w) and the determined battery temperature (453b).Type: ApplicationFiled: February 8, 2022Publication date: December 12, 2024Applicant: Telefonaktiebolaget LM Ericsson (publ)Inventors: Konstantinos VANDIKAS, Lackis ELEFTHERIADIS, Kristijonas CYRAS, Athanasios KARAPANTELAKIS, Cecilia NYSTRÖM, Divya SACHDEVA, Marin ORLIC, Gabriella NORDQUIST
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Patent number: 12167327Abstract: The present disclosure relates to a method of controlling allocation of mobile devices (12-23) to frequency bands in a radio site (10), and a device (11) performing the method. In an aspect, a method of a radio base station (11) of controlling allocation of mobile devices (12-23) to frequency bands in a radio site (10) is provided. The method comprises estimating (S101) power consumption of the radio base station (11) caused by radio traffic of the mobile devices (12-23) in each frequency band of the radio site (10), determining (S102) whether or not power consumption of the radio base station (11) is decreased by reallocating at least one of the mobile devices (12-23) from one frequency band to another frequency band, while not exceeding a power headroom limit of said another frequency band, and if so reallocating (S103) said at least one mobile device from said one frequency band to said another frequency band.Type: GrantFiled: November 6, 2019Date of Patent: December 10, 2024Assignee: Telefonaktiebolaget LM Ericsson (publ)Inventors: Xiaoyu Lan, Lackis Eleftheriadis, Aneta Vulgarakis Feljan, Marin Orlic, Yang Zuo
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Publication number: 20240406835Abstract: There is provided a method for training a reinforcement learning system for optimising routing for a network including a plurality of Integrated Access and Backhaul (IAB) nodes connected to an IAB donor. The method includes acquiring observations characterising a current state of the plurality of IAB nodes, determining an action to be performed based on latest acquired observations, executing the action by initiating update of the routing information based on the determined action, acquiring observations characterising an updated state of the plurality of IAB nodes, determining a reward for the determined action, based on the updated state of the plurality of IAB nodes, storing an experience set, and training the reinforcement learning system to maximise reward with respect to an optimisation objective, using the one or more stored experience sets in the buffer.Type: ApplicationFiled: August 3, 2021Publication date: December 5, 2024Inventors: Athanasios KARAPANTELAKIS, Konstantinos VANDIKAS, Marin ORLIC, Maxim TESLENKO, Marios DAOUTIS, Alexandros NIKOU, Aneta VULGARAKIS FELJAN, Lackis ELEFTHERIADIS
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Publication number: 20240388945Abstract: According to an aspect, there is provided a method of operating a control node, the method including receiving, from a third party node, a request for a value of a first performance indicator relating to one or more subscribers or to a first network slice; receiving, from a first communication network, a first value of the first performance indicator relating to the one or more subscribers in the first communication network or to the first network slice in the first communication network; determining a reliability of the received first value using a performance indicator profile; and performing an action with respect to the received request based on the determined reliability of the received first value.Type: ApplicationFiled: September 17, 2021Publication date: November 21, 2024Inventors: Athanasios KARAPANTELAKIS, Maxim TESLENKO, Alexandros NIKOU, Lackis ELEFTHERIADIS, Marin ORLIC
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Patent number: 12127114Abstract: The present disclosure relates to a method of controlling power supply units (1-9) of a base station (10), and a device (20) performing the method. In an aspect, a method of a base station scheduling device (20) of controlling power supply units (1-9) of a base station (10) is provided. The method comprises acquiring (S102a-c) information indicating a value of voltage input to at least one of the power supply units (1-9) of the base station (10), determining (S103) from said value if the voltage input to said at least one of the power supply units (1-9) is sufficient, and if not determining (S104) power demand of the base station (10), and deactivating (S105) said at least one power supply unit, if remaining power supply units (1-9) is capable of supplying the demanded power.Type: GrantFiled: May 29, 2019Date of Patent: October 22, 2024Assignee: TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)Inventors: Lackis Eleftheriadis, Adam Bergkvist, Bin Sun, Marin Orlic, Athanasios Karapantelakis, Yang Zuo
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Publication number: 20240323826Abstract: A method performed by a first node (111). The method is for handling roaming of a wireless device (130) from a first communications network (101). The first node (111) operates in a communications system (100). The first node (111) determines (502) a second communications network (201) to be used by the wireless device (130) for roaming communications. The determining (502) is based at least on: a) a predicted energy supply by the wireless device (130) during a roaming period, and b) a predicted use of data by the wireless device (130) during the roaming period. The first node (111) provides (504) an indication of the determined second communications network (201) to at least one of: a second node (112) operating in the communications system (100) and the wireless device (130).Type: ApplicationFiled: July 15, 2021Publication date: September 26, 2024Applicant: Telefonaktiebolaget LM Ericsson (publ)Inventors: Athanasios KARAPANTELAKIS, Konstantinos VANDIKAS, Maxim TESLENKO, Lackis ELEFTHERIADIS, Aneta VULGARAKIS FELJAN, Xiajing LI, Gabriella NORDQUIST, Marin ORLIC, Alexandros NIKOU, Marios DAOUTIS
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Publication number: 20240320489Abstract: A computer-implemented method (500) for determining, using a machine learning (ML) model, extended reality (XR) notification types for delivering notification of an event to a user is provided.Type: ApplicationFiled: September 13, 2021Publication date: September 26, 2024Applicant: Telefonaktiebolaget LM Ericsson (publ)Inventors: Konstantinos VANDIKAS, Marin ORLIC, Kristijonas CYRAS, Alexandros NIKOU, Alessandro PREVITI
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Publication number: 20240291548Abstract: Methods and apparatus for beam management are provided. A computer-implemented method for beam management includes obtaining measurements of one or more properties of an environment, wherein the environment contains one or more User Equipments (UEs). The method further includes initiating transmission of the obtained property measurements to a machine learning (ML) agent hosting a ML model, and receiving the transmitted property measurements at the ML agent. The method also includes processing the received property measurements using the ML model to suggest one or more beam options for exchanging data with the one or more UEs, from among a plurality of beam options, and selecting, using the one or more suggested beam options, at least one of the one or more suggested beam options. The method additionally includes exchanging data with the one or more UEs using the selected beam options.Type: ApplicationFiled: July 5, 2021Publication date: August 29, 2024Inventors: Athanasios KARAPANTELAKIS, Konstantinos VANDIKAS, Maxim TESLENKO, Alexandros NIKOU, Aneta VULGARAKIS FELJAN, Lackis ELEFTHERIADIS, Marin ORLIC, Marios DAOUTIS
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Publication number: 20240273416Abstract: Methods and apparatus for addressing intents using machine learning (ML) are provided. A method of operation for a node implementing ML, wherein the node instructs actions in an environment in accordance with a policy generated by a ML agent, and wherein the ML agent models the environment, includes obtaining an intent, wherein the intent specifies one or more criteria to be satisfied by the environment. The method further includes determining an intent cluster from among a plurality of intent clusters to which the intent maps, the determination being based on the criteria specified by the intent, and setting initialisation parameters for a ML model to be used to model the intent, based on the determined intent cluster. The method also includes training the ML model using training data specific to the intent, and generating one or more suggested actions to be performed on the environment using the trained ML model.Type: ApplicationFiled: June 18, 2021Publication date: August 15, 2024Inventors: Jaeseong JEONG, Alexandros NIKOU, Ezeddin AL HAKIM, Anusha Pradeep MUJUMDAR, Marin ORLIC
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Publication number: 20240236845Abstract: Methods and systems for power supply unit (PSU) control. A method includes measuring one or more properties of the PSU to obtain property measurements, and initiating transmission of the property measurements to a machine learning (ML) agent hosting a trained ML model. The method further includes receiving the property measurements at the ML agent, and processing the received property measurements using the trained ML model to generate suggested actions to be taken by the PSU. The method further includes predicting the effect of each of the suggested actions on the measured PSU properties, and selecting a subset of the suggested actions predicted to have a significant impact on the measured PSU properties. The method further includes initiating transmission of the selected subset of suggested actions to the PSU, and performing, at the PSU, the selected subset of suggested actions.Type: ApplicationFiled: March 2, 2021Publication date: July 11, 2024Inventors: Lackis ELEFTHERIADIS, Arpit SISODIA, Athanasios KARAPANTELAKIS, Konstantinos VANDIKAS, Marin ORLIC, Oleg GORBATOV, Sunil Kumar VUPPALA
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Publication number: 20240154852Abstract: A computer-implemented method performed in a multi-agent system by a first network node is provided for transferring historical data from an operating agent to a second agent for an action controlling a performance of the multi-agent system. The method includes selecting at least one operating agent for transfer of historical data to the second agent. The historical data acquired from executions of an action by the at least one operating agent that at least partially fulfills an input parameter. The selecting is based on one or more criteria including (i) a performance of the at least one operating agent on the parameter or on a related parameter; (ii) an availability of the at least one operating agent; and (iii) an identity of an actuation target system for receipt of the action. The method further includes transferring the historical data to the second agent.Type: ApplicationFiled: March 15, 2021Publication date: May 9, 2024Inventors: Athanasios KARAPANTELAKIS, Maxim TESLENKO, Ahmet Cihat BAKTIR, Marin ORLIC, Leonid MOKRUSHIN, Marios DAOUTIS, Aneta VULGARAKIS FELJAN, Alexandros NIKOU, Lackis ELEFTHERIADIS
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Publication number: 20240137861Abstract: Methods and systems for power supply unit (PSU) control. A method includes measuring one or more properties of the PSU to obtain property measurements, and initiating transmission of the property measurements to a machine learning (ML) agent hosting a trained ML model. The method further includes receiving the property measurements at the ML agent, and processing the received property measurements using the trained ML model to generate suggested actions to be taken by the PSU. The method further includes predicting the effect of each of the suggested actions on the measured PSU properties, and selecting a subset of the suggested actions predicted to have a significant impact on the measured PSU properties. The method further includes initiating transmission of the selected subset of suggested actions to the PSU, and performing, at the PSU, the selected subset of suggested actions.Type: ApplicationFiled: March 2, 2021Publication date: April 25, 2024Inventors: Lackis ELEFTHERIADIS, Arpit SISODIA, Athanasios KARAPANTELAKIS, Konstantinos VANDIKAS, Marin ORLIC, Oleg GORBATOV, Sunil Kumar VUPPALA
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Publication number: 20240119369Abstract: There is provided a method performed by a central entity of a network. A first set of features is selected for a machine learning model to take into account when analysing data. The machine learning model is to be deployed at an edge entity of the network. The selection is based on first information indicative of data that is available for the machine learning model to analyse, second information indicative of features that are available for the machine learning model to take into account when analysing data, and contextual information associated with the network.Type: ApplicationFiled: February 17, 2021Publication date: April 11, 2024Inventors: Kristijonas CYRAS, Athanasios KARAPANTELAKIS, Marin ORLIC, Jörg NIEMÖLLER, Leonid MOKRUSHIN, Aneta Vulgarakis FELJAN, Ramamurthy BADRINATH
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Publication number: 20240095588Abstract: A method is provided for determining bias of machine learning models. The method includes: forming a training dataset including input data samples provided to a remote machine learning model developed using a machine learning process, and corresponding output data samples obtained from the remote machine learning model; training a local machine learning model which approximates the remote machine learning model using a machine learning process and the training dataset; and interrogating the trained local machine learning model to determine whether the remote machine learning model is biased with respect to one or more biasing data parameters.Type: ApplicationFiled: February 15, 2021Publication date: March 21, 2024Inventors: Konstantinos VANDIKAS, Aneta VULGARAKIS FELJAN, Athanasios KARAPANTELAKIS, Marin ORLIC, Selim ICKIN
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Publication number: 20240086766Abstract: A computer-implemented method performed by a network node is provided. The method includes receiving a request for retrieving or executing a machine learning (ML) model or a combination of ML models. The request includes a first description of a specified output feature and specified input data type and distribution of input values for a ML model or combination of ML models. The method further includes obtaining an identification of a ML model, or a combination of ML models, having a second description that at least partially satisfies a match to the first description; identifying a candidate ML model, or combination of ML models, that produces the specified output feature of the first description based on a comparison of the first and second descriptions. The method further includes selecting a third description of the identified candidate ML model, or combination of ML models, based on a convergence.Type: ApplicationFiled: January 29, 2021Publication date: March 14, 2024Inventors: Athanasios KARAPENTELAKIS, Alessandro PREVITI, Konstantinos VANDIKAS, Lackis ELEFTHERIADIS, Marin ORLIC, Marios DAOUTIS, Maxim TESLENKO, Sai Hareesh ANAMANDRA
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Publication number: 20240089182Abstract: A method performed by a node in a communications network for determining a target network configuration for use in providing services to a first operator on the communications network. The method includes obtaining characteristics of the first operator; obtaining, for a plurality of previous operators in the communications network, characteristics of the previous operators and corresponding target network configurations used for the respective previous operators; matching the first operator to a second operator selected from the previous operators based on similarity between the characteristics of the first operator and the characteristics of the previous operators; and determining the target network configuration for the first operator, based on a target network configuration used for the second operator.Type: ApplicationFiled: February 9, 2021Publication date: March 14, 2024Inventors: Kristijonas CYRAS, Marin ORLIC, Aneta VULGARAKIS FELJAN, Saurabh SINGH
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Patent number: 11916931Abstract: A method of operating a protection node for protecting a pattern classification node from malicious requests may be provided. The protection node may receive, from a user node, a request containing an original pattern to be classified by a machine learning algorithm performed by the pattern classification node. The protection node may add noise to the original pattern to generate a noisy pattern. The protection node may obtain a first classification of the noisy pattern based on processing of the noisy pattern by a first clone of the machine learning algorithm at the protection node; obtain a second classification of the original pattern based forwarding the request for processing of the original pattern by the machine learning algorithm performed at the pattern classification node; and compare the first and second classifications to determine whether the first and second classifications satisfy a defined similarity rule. The protection node may use the comparison to manage the request from the user node.Type: GrantFiled: April 24, 2019Date of Patent: February 27, 2024Assignee: Telefonaktiebolaget LM Ericsson (publ)Inventors: Konstantinos Vandikas, Leonid Mokrushin, Maxim Teslenko, Daniel Lindström, Marin Orlic
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Patent number: 11894990Abstract: A computer implemented method performed by a node in a communications network comprises obtaining Key Performance Indicator, KPI, targets for a plurality of KPIs, in the communications network, and determining relationships between the KPIs using a model trained using a graph-based machine learning process. Each relationship describes a manner in which changing a network configuration to alter a first one of the plurality of KPIs affects a second one of the plurality of KPIs. The method then comprises determining one or more conflicts between the KPI targets, using the relationships.Type: GrantFiled: September 30, 2020Date of Patent: February 6, 2024Assignee: Telefonaktiebolaget LM Ericsson (publ)Inventors: Alessandro Previti, Kristijonas Cyras, Yifei Jin, Pedro Batista, Aneta Vulgarakis Feljan, Marin Orlic
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Publication number: 20240015553Abstract: There is provided a method for estimating a total energy consumption of a user equipment (UE) in a network. The method is performed by a network node. A total energy consumption for the UE is estimated (102) based on a resource usage for the UE and a measure of energy consumed by a base station of the network serving the UE in communicating with the UE. The resource usage for the UE is reported to the network node by the UE and/or the base station, and the measure of energy consumed by the base station is reported to the network node by the UE and/or the base station.Type: ApplicationFiled: November 11, 2020Publication date: January 11, 2024Inventors: Lackis Eleftheriadis, Alexandros Nikou, Cecilia Nyström, Kristijonas Cyras, Marin Orlic