Nokia Technologies Oy

Nokia Solutions and Networks Oy

Motorola Corporation

Motorola Mobility LLC

Wsou Investments, LLC

Jun Tan

Rapeepat Ratasuk

Nitin R Mangalvedhe

Weidong Yang

Frederick W Vook

Jie Chen

Amitabha Ghosh

Simon Svendsen

William J Hillery

Amitava Ghosh

Eugene Visotsky

Ryan Keating

Fan Wang

Mark Conrad Cudak

Benny Vejlgaard

Johannes Harrebek

Gilsoo Lee

Fred Vook

Claudio Rosa

Nuno Manuel Kiilerich Pratas

Xiaoyi Wang

Daniel Medina

Torsten Wildschek

Chandrasekar Sankaran

Qiping Zhu

Athul Prasad

Bill Hillery

Weimin Xiao

Timo Erkki Lunttila

Sami-Jukka Hakola

Ling Yu

Jorma Johannes Kaikkonen

Karri Markus Ranta-Aho

Timothy A Thomas

Klaus Hugl

Matthew Baker

Samantha Caporal Del Barrio

Poul Olesen

Christian Rom

Marco Maso

Anup K Talukdar

Stanley J Benes

Axel Mueller

Tzu-Chung Hsieh

Kim Nielsen

Stepan Kucera

Lawrence A Downing

Dan Park

Tzu-Chung (Frank) Hsieh

Dennis Edwards

Ye Luo

Bangliang Zhang

Wenqin Huang

Frederick Vook

In some example embodiments, there may be provided a method that includes receiving, by a machine learning encoder as part of a training phase, channel state information as data samples; generating, by the machine learning encoder, a latent variable comprising a log likelihood ratio value representation for the channel state information, wherein the latent variable provides a lower dimension binary representation when compared to the received channel state information to enable compression of the received channel state information; generating, by the binary sampler, a binary coding value representation of the latent variable, wherein the binary coding value converts the latent variable to a binary form; and generating, by the machine learning decoder, a reconstructed channel state information, wherein the generating is based in part on the binary coding value representation of the latent variable generated by the binary sampler. Related systems, methods, and articles of manufacture are also disclosed.

Binary variational (biv) CSI coding

Embodiments of the present disclosure provide for selecting a first candidate beam for communication with a first transmitter (Tx) user equipment (UE) and a second candidate beam for communication with a second Tx UE; determining a first radio resource expected to be used by the first Tx UE for a first radio transmission to a receiver UE and a second radio resource expected to be used by the second Tx UE for a second radio transmission to the receiver UE; determining that the first radio resource overlaps in time with the second radio resource, and that the first candidate beam does not match the second candidate beam; and determining whether to: receive the first radio transmission using the first candidate beam, receive the second radio transmission using the second candidate beam, or receive the first radio transmission and the second radio transmission using a third candidate beam.

Enhanced beam selection for NR sidelink communication in FR2

Certain example embodiments provide systems, methods, apparatuses, and computer program products for associating sensing information with a user (e.g., a cellular user) by, for example, determining information from both a sensing node and a communication node, then associating the a sensed object and the communication node based on the information. Additionally, or alternatively, some embodiments provide systems, methods, apparatuses, and computer program products for radar and sensing-based positioning.

Associating sensing information with a user

A method includes receiving, at a first network device from a second network device, a request to probe a network connected to the first network device. The request includes an identification of the second network device. The method also includes determining whether the second network device is authenticated to the network based on the identification. The method further includes in accordance with a determination that the second network device is authenticated to the network, causing a response to the received request to be transmitted to the second network device to enable a connection of the second network device to the network. In this way, the enrollee network device can be onboarded to the existing network in an easy and time-saving manner.

Methods, devices, apparatuses and computer readable media for connecting to network

A method may include determining, by a user equipment, a beam alignment reference value and a beam alignment test value; and determining, by the user equipment, based on the beam alignment reference value and the beam alignment test value, that a user equipment receive beam used by the user equipment is not aligned with a transmit beam of the base station.

Beam alignment verification for wireless networks

A user equipment includes at least one processor and at least one memory including computer program code. The at least one memory and the computer program code are configured to, with the at least one processor, cause the user equipment to: select radio resources for a first sidelink transmission to a first user equipment based on sidelink control information from a second user equipment, the sidelink control information including at least beam information for a second sidelink transmission by the second user equipment; and perform the first sidelink transmission to the first user equipment on the selected radio resources.

Sidelink beam information sensing and transmission

Methods and apparatus, including computer program products, are provided for increasing uplink beamforming gain. In one aspect there is provided a method of identifying user equipment which makes use of the proposed new beam-sweeping random access channel procedure that enables the base station to identify the best uplink transmit beam rather than the first beam that the base station managed to detect. Related apparatus, systems, methods, and articles are also described.

Methods for UL RACH coverage extension

Systems, methods, apparatuses, and computer program products for machine learning based channel state information (CSI) estimation and feedback configuration are provided. A method may include learning channel state information feedback from one or more user equipment as time series data. The method may also include building a predictive model for user equipment feedback based on the learned channel state information feedback. The method may further include configuring a channel state information trigger with the one or more user equipment based on the predictive model. In addition, the method may include signaling predicted channel state information to the one or more user equipment.

Machine learning based channel state information estimation and feedback configuration

A method comprises: determining, from an indication that an initial uplink beam between a user equipment and a network node is misaligned with respect to a downlink beam between the network node and the user equipment, at least one misalignment angle between the initial uplink beam and the downlink beam; and reconfiguring the uplink beam based on the misalignment angle to produce a reconfigured uplink beam.

Uplink beam reconfiguration

A method includes receiving, by a first user device in a wireless network, an indication of availability of a pre-trained model that estimates a channel between a second user device and a network node; receiving, by the first user device, information relating to the pre-trained model; determining, by the first user device, channel estimation information based at least on the information relating to the pre-trained model; and performing at least one of the following: transmitting, by the first user device, a report to the network node including the channel estimation information; or receiving data, by the first user device from the network node, based on the channel estimation information.

Model transfer within wireless networks for channel estimation

Systems, methods, apparatuses, and computer program products for initialization and operation of intelligent reflecting surface. The method may include transmitting a synchronization signal block burst to a reflection surface device. The method may also include receiving a first measurement report of the synchronization signal block burst received at the reflection surface device. The method may further include determining a transmit beam for a subsequent synchronization signal block burst based on a highest strength of signals in the synchronization signal block burst. In addition, the method may include receiving a second measurement report of the subsequent synchronization signal block burst. Further, the method may include determining an arrival angle of the subsequent synchronization signal block burst at the reflection surface device. The method may also include establishing a connection with the reflection surface device based on the transmit beam and the arrival angle.

Initialization and operation of intelligent reflecting surface

In accordance with example embodiments of the present disclosure there is at least a method and apparatus to perform communicating between a network node and a user equipment of a communication network, authorization for the user equipment to join a group of user equipment to enable the user equipment to receive multicast configurations for multicast signaling authorized for the group, wherein the authorization is based on configurations in common between the user equipment and the group of user equipment; and based on the joining, utilizing the configurations in common to produce signaling between the communication network and the user equipment to receive multicast configurations for the multicast signaling authorized for the group.

Machine learning based multicast user grouping

A technique includes receiving an uplink grant that indicates a size (M) of uplink resources allocated to the user device sub-band precoder indications; determining a set of uplink sub-band allocation parameters, including at least: a total number of bits (N) for sub-band precoder indications (where N remains constant for different values of M); a number of bits (m) per each of the sub-band precoder indications; and a sub-band size (J) for each of the one or more sub-bands; wherein at least one of the number of bits (m) per sub-band precoder indication and the sub-band size (J) for each of the one or more sub-bands changes or is a different value based on different sizes (M) of uplink resources; decoding each of the one or more sub-band precoder indications; and precoding, based on the one or more sub-band precoder indications, data for transmission via the one or more sub-bands.

Determination of sub-band allocation parameters for wireless networks

Systems, methods, apparatuses, and computer program products for intelligent reflecting surface configuration. The method may include scanning for synchronization signal blocks and reflection surface synchronization signal blocks during a cell search at a frequency on a synchronization raster. The method may also include determining whether a synchronization signal block or a reflection surface synchronization signal block is detected as a result of the scanning. The method may further include selecting a detected synchronization signal block or a detected reflection synchronization signal block based on a measurement metric of the detected synchronization signal block and the detected reflection synchronization signal block. In addition, the method may include performing an access procedure with a network element using the selected synchronization signal block or the selected reflection synchronization signal block.

Intelligent reflecting surface configuration

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Lake Zurich, IL, United States of America

Jun Tan