Nokia Technologies Oy

Nokia Solutions and Networks Oy

Nvidia Corporation

Alcatel Lucent

Jakob Hoydis

Faycal Ait Aoudia

Mathieu Goutay

Sebastian Cammerer

Pavan Koteshwar Srinath

Stephan Ten Brink

Luca Rose

Alvaro Valcarce Rial

Sebastian Dörner

Maximilian Stark

Alexander Georg Keller

Suresh Kalyanasundaram

Luc Dartois

Mikko Johannes Honkala

Veronique Capdevielle

Dani Johannes Korpi

Joerg Schaepperle

Osman Aydin

Richa Gupta

Lorenzo Maggi

Janne Matti Juhani Huttunen

Andreas Wich

Ori Shental

Matthijs Jules Van Keirsbilck

Vahid Aref

Maximilian Arnold

Stefan Schibisch

Paul Tourniaire

Marc Gauger

Ahmed Elkelesh

Abderrahmane Tchikou

Fayçal Ait Aoudia

Neural network-based structures for action user equipment device detection, estimation of time-of-arrival, and estimation of carrier frequency offset utilized with the narrowband physical random-access channel of wireless communication systems. The structure includes a neural network to generate predictions of active user equipment devices, and a twin neural network to generate time-of-arrival predictions for signals from the user equipment devices and carrier frequency offset predictions for signals from the user equipment devices.

Deep learning-based wireless communication synchronization structures

A retransmission method in a communication system, wherein the communication system comprises at least one transmitter and at least one receiver with a communication channel between the transmitter and the receiver, the method comprising: utilising in the transmitter a transmitter algorithm with trainable weights and in the receiver a receiver algorithm with trainable weights; generating () by the transmitter symbols to be transmitted based on a message to be sent and feedback received from the receiver and transmitting the symbols; generating () by the receiver a predicted message based on the received symbols, evaluating () the predicted message based on a criterion and providing (), utilising an algorithm with trainable weights, feedback symbols as a response to the transmitter if the evaluation indicates the predicted message is not acceptable.

Apparatuses and methods for providing feedback

According to the present disclosure, a first dataset comprising CSI measurements associated with first UEs and actual positions of the first UEs within a coverage region of a network node is received. Then, a second dataset comprising at least one CSI measurement associated with at least one second UE whose position within the coverage region of the network node is to be estimated is received. After that, based on the first and second datasets, the at least one CSI measurement associated with the at least one second UE is assigned to one of the first UEs that appears closest to the at least one second UE. Finally, the position of the at least one second UE within the coverage region of the network node is estimated by using a semi-supervised machine learning algorithm that receives the first and second datasets and the actual position of the closest first UE.

Apparatus and method for user equipment positioning and network node using the same

A method including determining types of radio access technologies supported by the apparatus, wherein the apparatus supports at least two types of radio access technologies, providing inputs to machine learning models, wherein the machine learning models include a corresponding machine learning model to each type of radio access technology, and for each type of radio access technology parameters associated with that type of radio access technology are provided as an input to the corresponding machine learning model, and obtaining, for each type of the radio access technologies, a power consumption estimate provided by the corresponding machine learning model.

Estimating power consumption of different type of radio access technology

An apparatus, method and computer program is described comprising: initialising parameters of a transmission system, wherein the transmission system comprises a transmitter, a channel and a receiver, wherein the transmitter includes a transmitter algorithm having at least some trainable weights and the receiver includes a receiver algorithm having at least some trainable weights; updating trainable parameters of the transmission system based on a loss function, wherein the trainable parameters include the trainable weights of the transmitter and the trainable weights of the receiver and wherein the loss function includes a penalty term; quantizing said trainable parameters, such that said weights can only take values within a codebook having a finite number of entries that is a subset of the possible values available during updating; and repeating the updating and quantizing until a first condition is reached.

End-to-end learning in communication systems

An apparatus, method and computer program is described including initialising trainable parameters of a receiver of a transmission system, wherein the receiver includes a demodulation module for demodulating received symbols, a quantization module for generating quantized versions of the demodulated symbols and a decoder for generating a decoded output derived from the quantized versions of the demodulated symbols, wherein the demodulation module has at least some trainable weights and the quantization module has at least some trainable weights; receiving a first training sequence of messages at the receiver; obtaining or generating a loss function; and updating at least some of the trainable parameters of receiver based on the loss function, wherein updating at least some of the trainable parameters of receiver includes updating at least some of the trainable weights of the demodulation module and updating at least some of the trainable weights of the quantization module.

Learning in communication systems

An apparatus, computer program and method is described including receiving data at a receiver of a communication system, generating an estimate of the data as transmitted by a transmitter of the transmission system (wherein generating the estimate includes a receiver algorithm having at least some trainable weights), generating a refined estimate of the transmitted data, based on said estimate and an error correction algorithm (wherein, in an operational mode, said estimate of the data as transmitted is generated based on the received data and said refined estimate); and generating, in the operational mode, a revised estimate of the transmitted data on each of a plurality of iterations of said generating an estimate of the transmitted data until a first condition is reached.

Iterative detection in a communication system

An apparatus, method and computer program is described including circuitry configured for using a transmitter algorithm to convert one or more inputs at a transmitter of a transmission system into one or more data symbols, wherein: the transmission system includes the transmitter implementing said transmitter algorithm, a channel and a receiver including a receiver algorithm; the transmitter algorithm includes trainable parameters for converting one or more received data symbols into one or more outputs; and the transmitter algorithm is stochastic.

Transmitter algorithm

The present subject matter relates to a receiver including a detector for receiving a signal from a transmitter. The detector includes a set of one or more settable parameters, and circuitry configured for implementing an algorithm having trainable parameters. The algorithm is configured to receive as input information indicative of a status of a communication channel between the transmitter and the receiver and to output values of the set of settable parameters of the detector. The detector is configured to receive a signal corresponding to a message sent by the transmitter and to provide an output indicative of the message based on the received signal and the output values of the set of settable parameters of the detector.

Receiver for a communication system

An apparatus, method and computer program is described including: receiving, at a receiver of a transmissions system, transmitted signals from each of a plurality of transmitters, wherein each transmitter communicates with the receiver over one of a plurality of channels of the transmission system, wherein each transmitter includes a transmitter algorithm having at least some trainable weights, wherein each transmitter algorithm has the same trainable weights and wherein each of the transmitted signals is based on a perturbed channel symbol generated at the respective transmitter, wherein the channel symbols and perturbations are known to the receiver; updating the weights of the transmitter algorithm, at the receiver, based on a loss function; providing the updated weights to each transmitter of the transmission system; and repeating the receiving and updating until a first condition is reached.

Training in communication systems

In one embodiment, a trainable logic includes determination logic configured to determine a plurality of available receiver configurations and associate each receiver configuration with a context matrix; codebook logic configured to select a quantisation codebook to be used by the trainable logic for the context matrices; and learning logic configured to learn from a training dataset including a plurality of received signal samples relevant to reconstruction of a transmitted message. The learning logic is configured to generate, from the training dataset, a set of superposed parameters and context matrices corresponding to the plurality of available receiver configurations and a set of contextual parameters for each context; quantize the context matrices according to the quantisation codebook; and repeat the generation of superposed parameters, context matrices and quantization of context matrices until a stop criterion is met.

Signal demapping

To obtain delay spread estimations and/or Doppler spread estimations, data representing received data is input to at least one trained model, the trained model outputting spread estimations.

Estimating delay spread and doppler spread

A central node receives, from each of a plurality of access points providing access for user equipments to a communication network, a result of a measurement related to a position of a user equipment, receives additional information comprising at least one of an evaluation of the result of the measurement and a reference towards which the measurement was done, and calculates the position of the user equipment by combining the results of the measurements received from the plurality of access points using at least one of the evaluation and the reference.

Positioning with multiple access points

A method comprising receiving a modulated radio signal transmitting coded information bits, performing demodulating on the modulated radio signal, wherein demodulating comprises performing orthogonal time frequency space demodulation, performing equalization on the demodulated radio signal to obtain equalized symbols, obtaining log-likelihood ratios for the coded information bits from the equalized symbols using a trained machine learning model, and reconstructing the coded information bits.

Transmitting of information in wireless communication

Different solutions for an apparatus comprising a predictor predicting decodability of received symbols are disclosed. Decoding is performed based on the prediction.

Growing community of inventors

Paris, France

Jakob Hoydis