Nokia Solutions and Networks Oy

Nokia Siemens Networks Oy

Nokia Technologies Oy

Nokia Siemens Networks Gmbh & Co. Kg

Fraunhofer-gesellschaft Zur Foerderung Der Angewandten Forschung E.v.

Nokia Solutions and Networks Gmbh & Co. Kg

Siemens Aktiengesellschaft

Adtran Gmbh

Hmd Global Oy

Nokia Siemens Networks Gmbh & Co Kg

Nokia Solutions and Networks Management International Gmbh

Wolfgang Zirwas

Egon Schulz

Thomas Haustein

Volker Jungnickel

Berthold Panzner

Lars Thiele

Michael Faerber

Rakash Sivasiva Ganesan

Josef Martin Eichinger

Hui Li

Thomas Wirth

Mihai Enescu

Simone Redana

Malte Schellmann

Yi Jie Xue

Wolfgang Mennerich

Tao Liu

Ping Li

Sabine Roessel

Jijun Luo

Ruediger Halfmann

Thomas Hindelang

Andreas Forck

Bernhard Raaf

Tero Henttonen

Elena Virtej

Mikko Saily

Bernhard Wegmann

Dan Yu

Martin Goldberg

Clemens Von Helmolt

Amir Mehdi Ahmadian Tehrani

Helmut Heinz

Xinglin Wang

Rakash Sivasivaganesan

Jacek Gora

Holger Gaebler

Muhammad Bilal Amin

Jee Hyun Kim

Markus Staudacher

Xiaoben He

Krystian Safjan

Oliver Cyranka

Rupert Herzog

Frank Frederiksen

Lars Dalsgaard

Zexian Li

Frederick W Vook

Ingo Viering

Eugene Visotsky

Oana-Elena Barbu

Thorsten Wild

Keeth Saliya Jayasinghe Laddu

Ryan Keating

Athul Prasad

Tao Tao

Mieszko Chmiel

Mikko Säily

Martin Weckerle

Matti Tapani Kiiski

Hermann Rohling

William J Hillery

Youngsoo Yuk

Lan Jun Wang

Matthias Lott

Paul Walter Baier

Jani Matti Johannes Moilanen

István Zsolt Kovács

Elena Costa

Tobias Weber

Rüdiger Halfmann

Daejung Yoon

Teemu Mikael Veijalainen

Ottmar Aumann

Stephan Saur

Thomas Ahrndt

Matthias Schnitter

Gerhard Kramer

Luis Guilherme Uzeda Garcia

Xiaomao Mao

Bernhard Schweyer

Andreas Ibing

Yan Gao

Tobias Giebel

Dalia-Georgiana Popescu

Patrick Marsch

Hanguang Wu

Bernard Raaf

Stefan Schiffermüller

Harianto Wijaya

Mattias Lampe

Rana Ahmed

Brenda Vilas Boas

Rakesh Sivasivaganesan

A method for performing multiple-input and multiple-output channel estimation includes: generating, using a first machine-learning model, an initial set of estimated channel information from a first input set of channel information, wherein the first input set of channel information corresponds to a first plurality of radio-frequency chains, and wherein the estimated set of channel information corresponds to the first plurality of radio-frequency chains and a second plurality of radio-frequency chains; generating, using a second machine-learning model, a set of estimated channel phases from the initial set of estimated channel information and a second input set of channel information, wherein the second set of input channel information corresponds to the second plurality of radio-frequency chains; and combining the initial set of estimated channel information and the set of estimated channel phases to generate an enhanced set of estimated channel information.

Channel estimation using machine learning

Embodiments of the present disclosure relate to beam selection during downlink positioning. According to embodiments of the present disclosure, the terminal devices are grouped based on their characteristics and the anchor terminal device in the group determines the best transmitting beam of the neighbor network device. The non-anchor terminal device uses the assistance information of the best transmitting beam determined by the anchor terminal device. In this way, cost for the terminal device to receive and measure the PRS during downlink positioning has been reduced.

Beam selection during downlink positioning

According to an example embodiment, an example method may include estimating, by a receiver device in a wireless network, a set of one or more active beams for the receiver device based on an estimated receive power of each active beam to obtain an estimated set of one or more active beams for the receiver device; receiving, by the receiver device, verification information associated with the estimated set of one or more active beams for the receiver device; and, verifying, by the receiver device based on the verification information, the estimated set of one or more active beams for the receiver device.

Efficient beam management for wireless networks

Disclosed is a method comprising providing a primary cell capable of serving a terminal device; determining a candidate secondary cell group, comprising one or more cells that have coverage area within the coverage are of the primary cell; determining, from the candidate secondary cell group, a cell that, based on information obtained before or during a connection setup signalling, has the highest probability of being successfully configured as a secondary cell for an inter-site carrier aggregation for the terminal device; and configuring the cell as the secondary cell for the inter-site carrier aggregation.

Enhanced blind configuration of a cell

A method may include transmitting, by a network node within a wireless network to a reference node, a first precoded tracking signal based on estimated precoding weights that are estimated to provide a predetermined signal at the reference node; receiving, by the network node from the reference node, a message including information related to whether or not the predetermined signal was received at the reference node based, at least in part, on the first precoded tracking signal transmitted by the network node; performing the following, by the network node, if the predetermined signal was not received at the reference node: adjusting one or more transmission parameters of the network node, that is estimated to more accurately provide the predetermined signal at the reference node; and transmitting, by the network node, a second precoded tracking signal based on adjusted transmission parameters.

Precoding tracking for cell free massive MIMO

It is provided a method, including providing location information indicating a location of a terminal to a radio-independent localization and tracking system; evaluating at least one of environmental information and tracking information received from the radio-independent localization and tracking system with respect to the terminal in response to providing the location information; managing a resource for serving the terminal based on the at least one of the environmental information and the tracking information, wherein the environmental information includes information about an environment of the terminal, and the tracking information includes information about a track of the terminal.

Optimizing resource of a terminal allocation by computer vision

An example method, apparatus, and computer-readable storage medium are provided for exploration procedures for network optimization. In one example implementation, the method may include generating, by a first network element, exploration data, the exploration data being generated by the first network element for evaluating performance at a second network element; transmitting, by the first network element, the exploration data to the second network element; and receiving, by the first network element, exploration data feedback from the second network element, the exploration data feedback received from the second network element based on processing of the exploration data by the second network element. In another example implementation, the method may include receiving, by a second network element, exploration data from a first network element; generating, by the second network element, exploration data feedback, the exploration data feedback generated in response to and based on the exploration data received from the first network element; and transmitting, by the second network element, the exploration data feedback to the first network element.

Exploration data for network optimization

An example method, apparatus, and computer-readable storage medium are provided for early data transmission (EDT) random access (RA) procedure for dual connectivity or carrier aggregation. In an example implementation, the method may include receiving configuration information from a network node, initiating an early data transmission (EDT) random access (RA) procedure with one or more cells indicated in the configuration information, determining a secondary cell of the one or more cells as a preferred secondary cell, and transmitting data to the preferred secondary cell.

Early data transmission for dual connectivity or carrier aggregation

Inter-alia, an apparatus is disclosed comprising: at least one power amplifier coupled to at least one radio frequency power storage device, wherein the at least one power amplifier is configured to supply power to the at least one radio frequency power storage device, wherein the at least one power amplifier provides power to be used to amplify one or more radio frequency signals; wherein the at least one radio frequency power storage device is configured to store the power of the at least one power amplifier for a certain time period; and one or more antenna elements coupled to the at least one radio frequency power storage device, wherein the at least one radio frequency power storage device is configured to output stored power to at least one of the one or more antenna elements, wherein the power is output variably dependent upon a power demand of a required radio frequency power and/or amplitude needed to transmit the one or more radio frequency signals, wherein the power demand represents a power demand of the at least one antenna element to which the power is to be output. It is further disclosed an according method, and system.

Massive MIMO antenna array

Method, comprising receiving a terminal location information or a location-like information from a terminal; selecting one or more first pairs of prior channel information among one or more stored first pairs of prior channel information based on the terminal location information or the location-like information, respectively; inputting the terminal location information or the location-like information, respectively, and the selected one or more first pairs of prior channel information into a trained interpolation neural network to obtain a first estimation of a channel between the terminal and a base station as an output from the interpolation neural network; providing the weights of the trained neural network to the terminal; wherein each of the one or more first pairs of prior channel information comprises a location information related to a respective prior channel and a first representation of the respective prior channel.

Multiple channel CSI recreation

Methods, apparatus, and computer program products for a user equipment receiving CSI-RS and computing channel support information. Based on the CSI, the UE derives tap locations of channel support, depending on the observed channel and used subcarrier spacing. The deriving can additionally be done in relation to power class and window size. The UE apprises a base station of those tap locations of the channel support and also informs the base station of the strongest tap locations, which can be done in various ways comprising a bit mask. Methods, apparatus, and computer program products for a base station to send CSI-RS to a UE for computing CSI feedback. The CSI-RS resources can be for one or multiple transmit receive points. The base station receives back an indication of tap locations and a bit mask and builds information on tap locations by combining that received indication and bit mask.

Compression of tap location information for time domain explicit channel state information feedback in new radio

A synchronization signal is received at a user equipment from a base station. The user equipment synchronizes clocks between the user equipment and the base station using a combination of sample and sub-sample timing determined based at least in part on the synchronization signal. At a base station, the base station transmits toward a user equipment a synchronization signal. The base station synchronizes clocks between the base station and the user equipment using a combination of sample and sub-sample timing determined based on at least the synchronization signal. Methods, apparatus, software, and computer program products are disclosed.

Synchronizing TSN master clocks in wireless networks

An example of an embodiment is a method including: allocating, by a network node, an uplink non-orthogonal reference signal sequence to each of a plurality of user equipments within a cooperation area; receiving, from at least one user equipment of the plurality of user equipments, the uplink non-orthogonal reference signal allocated to the at least one user equipment; and determining channel state information for the at least one user equipment based on the received uplink non-orthogonal reference signal.

Enhanced sounding reference signal scheme

For a system with multiple Antenna Elements, AEs, to be used to transmit multiple symbols to multiple user equipments, UEs, and with multiple time sample positions, where the symbols occupy a time-frequency resource space and wherein at least some of the AEs are powered by constrained RF chains having functionalities that are simplified relative to full RF chains, precoder coefficients are generated for the multiple AEs taking into consideration multipath components of a signal to be transmitted from the multiple AEs to the multiple UEs. The generating forms precoder coefficients for individual ones of the constrained RF chains at corresponding certain time sample positions of the symbols. The signal is transmitted from the multiple AEs to the multiple UEs using FDMA at least by applying the generated precoder coefficients to the multiple AEs over the multiple time sample positions and time-frequency resource space of the symbols.

MU-MIMO precoder design and use for a wideband mMIMO system with constrained RF chains in a multi-path environment

A technique comprising: receiving a set of digital signals representative of a set of analogue electrical signals generated by radio transmissions in one or more antenna elements; generating at least two beamformed signals from the same set of digital signals; multiplying together said beamformed signals; and performing demodulation at least partially on an output of multiplying said beamformed signals.

Beamforming

Growing community of inventors

Munich, Germany

Wolfgang Zirwas