Telefonaktiebolaget Lm Ericsson (publ)

Henrik Rydén

Fredrik Gunnarsson

Sara Modarres Razavi

Xingqin Lin

Olof Liberg

Meng Wang

Zhenhua Zou

Iana Siomina

Pradeepa Ramachandra

Helka-Liina Määttanen

Yutao Sui

Helka-Liina Määttänen

Ritesh Shreevastav

Åke Busin

Yi-Pin Eric Wang

Johan Mikael Bergman

Reza Moosavi

Gino Masini

Torbjörn Wigren

Yufei W Blankenship

Erik A Eriksson

Kai-Erik Sunell

Stefano Sorrentino

Martin Hessler

Magnus Stattin

Siva Dharshan Muruganathan

Karl Norrman

Mattias Bergström

Mårten Sundberg

Niclas Wiberg

Riikka Susitaival

Mirsad Cirkic

Joel Berglund

Jonas Sedin

Sebastian Euler

Ali Zaidi

Sakib Bin Redhwan

Björn Hofström

Maksym Girnyk

Vijaya Yajnanarayana

Alexandros Palaios

Talha Khan

Hamed Farhadi

Xin Zhang

Stefan Rommer

David Sandberg

Abdulrahman Alabbasi

Roy Timo

Massimo Condoluci

Satyam Dwivedi

Michael Liljenstam

László Hévizi

Samuel Axelsson

Mats Zachrison

Ursula Challita

Johannes Nygren

Ricardo Da Silva Souza

Ankit Jauhari

Mattias BERGSTRÖM

Viktor Berggren

Roman Zhohov

A computer implemented method for detecting data (y) comprised in a part (x) of a received signal (w) of a communication system (), wherein the received signal (w) is associated with a population and where the part (x) of the received signal (w) is associated with a sub-population of the population, the method comprising: configuring (S) a first function (f) to determine a context (c) of the received signal (w), wherein the context (c) is indicative of a state of the received signal (w), configuring (S) a second function (f) to detect the data (y) based on the part (x) of the received signal, wherein the second function (f) is arranged to be parameterized by the context (c), and detecting (S) the data (y) by the first (f) and second (f) functions.

Context aware data receiver for communication signals based on machine learning

A computer implemented method in a communications network for determining location information about an actual location of a drone comprises obtaining a reported location of the drone at a first time point and obtaining a measurement of radio conditions between the drone and a node in the telecommunications network, at the first time point. The method then comprises predicting radio conditions at one or more locations related to the reported location of the drone, and determining the location information about the actual location of the drone based on the measured radio conditions and the predicted radio conditions.

Determining location information about a drone

A method for managing a first node in a communication network is disclosed, wherein the first node is operable to exchange traffic flows with other nodes in the communication network. The method includes using a Variational Autoencoder (VAE) to generate a predicted traffic distribution for the first node, wherein the VAE has been trained using information about historical data flows exchanged by the first node with at least one other node in the communication network, and configuring at least one radio resource parameter of the first node based on the obtained predicted traffic distribution for the first node. Also disclosed are a method including training a VAE and nodes and a computer program product suitable for carrying out such methods.

Managing a node in a communication network

Methods and apparatus in a communications network as described herein are suitable for determining a first model for use in predicting conditions on carrier frequencies in the communications network. In a method, network measurements are obtained () of conditions on a plurality of carrier frequencies. A first model is then determined () based on the obtained measurements that takes as input the conditions on a first subset of the plurality of carrier frequencies and outputs a prediction of the conditions on a second subset of the plurality of carrier frequencies, based on the conditions of the first subset of carrier frequencies.

Predicting conditions on carrier frequencies in a communications network

The present disclosure provides relates to methods and apparatus for handover optimization in a 5G context using reinforcement learning (RL). In contrast to the conventional handover methods, handovers between base-stations (BSs) are controlled using a centralized RL-based machine learning (ML) agent. This ML agent handles the radio measurement reports from the UEs and chooses appropriate handover actions in accordance with the RL machine learning framework to maximize a long-term utility.

Network nodes and methods performed therein for supporting handover of a wireless device

A computer-implemented method for acquiring new data samples and for maintaining a set of data samples in a database, wherein the set of data samples are configured to form input to a function associated with a predictive performance, the method including obtaining at least one relevance metric (M), where the relevance metric is indicative of an increase in the predictive performance of the function when using a data sample as input together with the set of data samples compared to when not using the data sample, obtaining a relevance criterion (C), where the relevance criterion identifies relevant data samples in a set of data samples based on the at least one relevance metric, signaling the at least one relevance metric (M) and the relevance criterion (C) to a data collecting network node.

Methods for processing data samples in communication networks

Methods performed by base stations and wireless devices for reducing RSRP and/or RSRQ measurements are disclosed. A method performed by a base station comprises indicating to a wireless device whether or not to perform cell selection and/or reselection measurements based on certain criteria. A method performed by a wireless device comprises determining whether or not to perform cell selection and/or reselection measurements based on certain criteria. Also disclosed are base stations and wireless devices configured to perform the methods.

UE procedures for reducing RSRP/RSRQ measurement in RRC idle mode and inactive state

In one aspect, network nodes maybe configured for improving predictability of application performance in a wireless communication system. A mobile network domain predicts an upcoming network event affecting a UE operating in the wireless communication system and notifies an application server (AS) providing an application service to the UE of the predicted network event. The AS may be notified through an Application Function (AF). The AS receives the notification and adjusts operation of the application service and/or a configuration of the UE, in response to the notification.

Notification of expected event

A method is performed by a wireless device for communication in a non-terrestrial network, NTN, comprising at least a first non-terrestrial network node and a second non-terrestrial network node. The method includes determining an estimated location of the wireless device. The method further includes obtaining first location information associated with the first non-terrestrial network node and second location information associated with the second non-terrestrial network node. The method further includes calculating an estimated signal time arrival difference based on the location of the wireless device, the first location information, and the second location information. The method further includes using the estimated signal time arrival difference to provision an NTN-specific synchronization signal/physical broadcast channel, SS/PBCH, block measurement timing configuration, NTN-SMTC.

Detection of neighboring satellites in wireless communication systems

Embodiments include methods for radio link monitoring (RLM) by a user equipment (UE) in a combined terrestrial and satellite wireless communication network. Such methods include, during a discontinuity in a connection between a UE and a terrestrial network node (TNN), performing RLM of the connection based on a second configuration. Such methods also include, before and/or after the discontinuity, performing RLM of the connection based on a first configuration. The connection includes a first link between the UE and a satellite and a second link between the satellite and the TNN. The discontinuity includes a link switch in at least one of the first link and the second link. The RLM before, during, and after the discontinuity can include various first, second, and third operations, respectively. Embodiments also include complementary methods performed by a TNN, as well as UEs and TNNs configured to perform operations corresponding to such methods.

Configuring radio link monitoring (RLM) for moving radio access network (RAN)

Methods performed by network nodes in a radio access network may be provided. A first network node may receive an identification of data models supported by a second network node. Each of the identified data models may include a data model for calculating a compact representation of data collected, at least one correlation parameter, and an accuracy metric. The first network node may transmit a request that the second network node calculate an identified one of the data models. The first network node may receive the calculated data model from the second network node. The first network node may evaluate the received calculated data model based on determining whether the received calculated data model correlates to at least one correlation parameter.

Methods for data model sharing for a radio access network and related infrastructure

In a first method, a wireless device estimates a delay profile of a channel impulse response, CIR, for a channel between a network node and the wireless device, compresses the delay profile using a compression function, and transmits the compressed delay profile. The compression function includes a first function and a quantizer. The first function is configured to receive input data and reduce a dimension of the input data. In a second method, a network node receives a compressed delay profile of CIR for a channel between a network node and a wireless device, decompresses the compressed delay profile using a decompression function, and estimates a position of the wireless device based on at least the decompressed delay profile. The decompression function includes a first function which is configured to receive input data and provide output data in a higher dimensional space than the input data.

Compression and decompression of delay profile

A system, method and non-transitory computer readable media for facilitating reduced signaling load with respect to UE location reporting in a non-terrestrial network (NTN) environment () interoperable with one or more terrestrial cellular communications networks. In one embodiment of an index-based location reporting scheme, a UE () may receive () a list of reference locations from a network node (), each identified with an index. Responsive to receiving the list of reference locations, the UE () may report to the network node () an index or a subset of indices of one or more reference locations that match a specific reporting criterion. In an example implementation, reporting of index-based location information by the UE () may be configurably triggered, e.g., periodically or in response to the occurrence of a particular event, condition, etc.

System and method for facilitating index-based positioning in a non-terrestrial network

The present disclosure relates to a method performed by a UE for enabling GNSS measurements. The UE and a network node are included in a NR communications network, and the NR communications network includes a NTN component. The UE performs a GNSS measurement according to a rule and using a GNSS receiver included in the UE. At least one of a UE position and an absolute time is an output of the GNSS measurement or is derivable from the GNSS measurement.

UE, network node and method for enabling GNSS measurements

A network node of a wireless communication network provides information to a User Equipment (UE) that allows the UE to adapt it radio measurements with respect to the network, to account for 'link changes.' Link changes are service-link and/or feeder-link changes arising from motion by the satellite(s) included in the Radio Access Network (RAN) portion of the network. The UE uses the provided information to adapt its radio measurements in conjunction with the link changes. Among the several advantages of accounting for satellite link changes in the manner contemplated are any one or more of reduced signaling going between the UE and the network, reduced battery consumption at the UE, mitigation of the effects of the link changes on the radio measurements, avoidance of spurious measurements, and an effective “automation” of the timed changeover by the UE to new measurement configurations, coincident with the occurrence of link changes.

User equipment, network node and methods therein for handling a moving radio access network

Growing community of inventors

Solna, Sweden

Henrik Rydén