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公开(公告)号：US12015507B2
公开(公告)日：2024-06-18
申请号：US18013586
申请日：2020-06-29
Applicant: Nokia Technologies Oy
Inventor： Faycal Ait Aoudia , Jakob Hoydis
CPC classification number: H04L25/03165 , H04B7/0615 , H04L25/03343
Abstract: 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.
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公开(公告)号：US11736899B2
公开(公告)日：2023-08-22
申请号：US17145955
申请日：2021-01-11
Applicant: Nokia Technologies Oy
Inventor： Pavan Koteshwar Srinath , Jakob Hoydis
CPC classification number: H04W4/029 , G01S5/0246
Abstract: An apparatus, method and computer program is described comprising: generating a first loss function component comprising comparing first location data with first location estimates, wherein the first location estimates are based on channel state data, wherein the first location estimates are generated using a model, and wherein the model comprises a plurality of trainable parameters; generating a second loss function component comprising comparing the first location data with second location estimates, wherein the second location estimates are based on channel state data that have been subjected to a first augmentation and wherein the second location estimates are generated using the model; generating a third loss function component comprising comparing third location estimates based on channel state data and fourth location estimates based on channel state data that have been subjected to a second augmentation, wherein the third and fourth location estimates are generated using the model; and training the trainable parameters of the model by minimising a loss function based on a combination of the first, second and third loss function components.
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公开(公告)号：US11483717B1
公开(公告)日：2022-10-25
申请号：US17707448
申请日：2022-03-29
Applicant: NOKIA TECHNOLOGIES OY
Inventor： Faycal Ait Aoudia , Jakob Hoydis , Dani Johannes Korpi , Janne Matti Juhani Huttunen , Mikko Johannes Honkala
Abstract: Disclosed is a method comprising providing a first resource grid as input to a first machine learning algorithm, obtaining a second resource grid as output from the first machine learning algorithm, and transmitting a signal comprising the second resource grid by using orthogonal frequency-division multiplexing modulation.
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公开(公告)号：US11070410B1
公开(公告)日：2021-07-20
申请号：US17149268
申请日：2021-01-14
Applicant: NOKIA TECHNOLOGIES OY
Inventor： Faycal Ait Aoudia , Jakob Hoydis
Abstract: To support a wide range of code rates in probabilistic shaping based modulation schemes solutions, a constellation which is at least based on a trained model is used in the modulation. Depending on an implementation the trained model may be a constellation comprising a plurality of sub-constellations with trained parameters as constellation points, or the trained model may be for generating a constellation and corresponding constellation points.
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公开(公告)号：US12124937B2
公开(公告)日：2024-10-22
申请号：US17295296
申请日：2018-11-22
Applicant: Nokia Technologies Oy
Inventor： Jakob Hoydis , Ori Shental
CPC classification number: G06N3/045 , H04B1/16 , H04B1/0003
Abstract: 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.
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Patent Agency Ranking
公开(公告)号：US12081324B2
公开(公告)日：2024-09-03
申请号：US17621747
申请日：2019-06-27
Applicant: Nokia Technologies Oy
Inventor： Faycal Ait Aoudia , Maximilian Stark , Jakob Hoydis
CPC classification number: H04L1/0002 , G06N3/045 , G06N3/047 , G06N3/08 , H04L25/49
Abstract: 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.
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公开(公告)号：US11870530B2
公开(公告)日：2024-01-09
申请号：US17279361
申请日：2018-09-28
Applicant: Nokia Technologies Oy
Inventor： Jakob Hoydis , Luca Rose
IPC: H04W72/044 , H04B7/06 , H04B7/08
CPC classification number: H04B7/0695 , H04B7/088 , H04W72/046
Abstract: A method, apparatus and computer program is described, comprising: obtaining a first beam alignment dataset, wherein the first beam alignment dataset comprises measurement data for a first plurality of beam pair transmissions, wherein each beam pair transmission of the first plurality is between one of a plurality of communication beams of a first user device and one of a plurality of communication beams of a base station and wherein the first plurality of beam pair transmissions is a subset of all available beam pair transmissions between the first user device and the base station; and selecting a first beam pair combination for communications between the first user device and the base station, wherein the first beam pair combination comprises one of the plurality of beams of the first user device and one of the plurality of beams of the base station, wherein the means for selecting the first beam pair combination comprises a machine-learning model trained with a second beam alignment dataset.
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公开(公告)号：US11695202B2
公开(公告)日：2023-07-04
申请号：US17252074
申请日：2018-07-13
Applicant: Nokia Technologies Oy
Inventor： Jakob Hoydis , Faycal Ait Aoudia
CPC classification number: H01Q3/01 , H01Q1/246 , H01Q21/061
Abstract: An apparatus, method and computer program product is disclosed. The apparatus may comprise means for receiving a performance metric for an antenna array comprised of a plurality of radiating elements, the performance metric being based on performance data associated with the antenna array, the antenna array having a radiating configuration represented by configuration parameters. The apparatus may also comprise means for updating the configuration parameters dependent on the received performance metric by means of estimating new configuration parameters for moving the performance metric towards a target value. The apparatus may also comprise means for re-configuring the radiating configuration of the antenna array based on the updated configuration parameters such that the physical geometry of the antenna array is changed.
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公开(公告)号：US11575547B2
公开(公告)日：2023-02-07
申请号：US16623072
申请日：2018-05-15
Applicant: Nokia Technologies Oy
Inventor： Jakob Hoydis , Sebastian Cammerer , Sebastian Dörner
Abstract: A method and devices for configuring a data transmission network are disclosed. The method is for configuring a data transmission network, executed by a configuration device, wherein the data transmission network comprises at least one transmitter, at least one receiver with a communication channel between the transmitter and the receiver, the method comprising: training a machine learning model of the data transmission network, wherein the machine learning model comprises at least a transmitter model including a transmitter neural network, a channel model, and a receiver model including a receiver neural network by providing a message within a sequence of messages; generating a group of transmission symbols for each message in the sequence of messages using the transmitter neural network; concatenating the groups of transmission symbols together as a sequence of transmission symbols; simulating transmission of the sequence of transmission symbols over the communication channel using the channel model to the receiver; analysing a sequence of received symbols using the reception neural network to generate a decoded message; and updating the machine learning model based on an output of said reception neural network. In this way, the machine learning model can be trained using representative sequences of message, which improves performance when deployed in a real network.
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公开(公告)号：US12273854B2
公开(公告)日：2025-04-08
申请号：US17924947
申请日：2021-05-07
Applicant: NOKIA TECHNOLOGIES OY
Inventor： Pavan Koteshwar Srinath , Jakob Hoydis
Abstract: 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.
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