Nokia Technologies Oy

Amir Mehdi Ahmadian Tehrani

Marco Maso

Nhat-Quang Nhan

Wolfgang Zirwas

Karri Markus Ranta-Aho

Keeth Saliya Jayasinghe Laddu

Axel Mueller

Zexian Li

Mihai Enescu

Oana-Elena Barbu

Thorsten Wild

Johannes Harrebek

Samantha Caporal Del Barrio

Rakash Sivasiva Ganesan

Alessio Marcone

Xiaomao Mao

Leonardo Chiarello

Sajad Rezaie

Simon Svendsen

Brenda Vilas Boas

Rana Ahmed

Example embodiments provide improved spatial beam predictions based on multiple assistance information parameters. An apparatus may be configured to configure a plurality of subspaces and corresponding machine learning models based on a dataset comprising multiple assistance information parameters, select a subset of the subspaces for inference by the machine learning models based on available assistance information, and determine a preferred spatial beam prediction based on the inferences. Apparatuses, methods, and computer programs are disclosed.

Apparatuses and methods for spatial beam prediction with multiple assistance information

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

According to an example aspect of the present invention, there is provided an apparatus, such as a user equipment, including at least one processing core, at least one memory including computer program code, the at least one memory and the computer program code being configured to, with the at least one processing core, cause the apparatus at least to initiate a random access process with a network by transmitting an initial random access message to the network, receive a random access response from the network, the random access response including a timing advance command bit sequence, determine from the timing advance command bit sequence a timing advance value and a configuration parameter, and transmit a request message to the network at least once in compliance with the configuration parameter.

Transmission of parameter

Example embodiments of the present disclosure relate to methods, devices, apparatuses and computer readable storage medium for Random Access Channel (RACH) occasion (RO) dropping. In a method, a device determines a set of valid random access channel occasions (ROs) for a physical random access channel (PRACH) transmission with preamble repetitions. The device determines that the PRACH transmission is to be dropped in at least one RO of the set of valid ROs. The device determines one or more ROs of the set of valid ROs for calculation of a random access radio network temporary identifier (RA-RNTI), the one or more ROs for calculation of the RA-RNTI are not conditioned to whether the corresponding PRACH transmission over the one or more ROs is to be dropped. Then the device calculates the RA-RNTI based on the determined one or more ROs of the set of valid ROs.

RO dropping and RA-RNTI calculation

A method comprises: determining, at a user equipment, user equipment current operating conditions; identifying whether an antenna configuration for maintaining correspondence between user equipment transmission and reception beams is stored by the user equipment for the user equipment current operating conditions; and when the antenna configuration is identified, applying the antenna configuration for maintaining correspondence between the user equipment transmission and reception beams.

User equipment beam correspondence

Systems, methods, apparatuses, and computer program products for differentiating between user equipment, having different capabilities are provided. One method may include transmitting, by a user equipment, a first transmission to a network node. The method may include indicating at least one type of the user equipment. For example, the indicating may include at least one of transmitting the first transmission on a frequency resource that has been modified in frequency domain from an allocated frequency resource by a shifting factor, and/or transmitting the first transmission on the allocated frequency resource and power scaling at least one resource element of the allocated resource.

Methods and apparatuses for differentiating between user equipment

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

Growing community of inventors

Karlsfeld, Germany

Amir Mehdi Ahmadian Tehrani