Nokia Technologies Oy

Nokia Solutions and Networks Oy

Pavan Koteshwar Srinath

Jakob Hoydis

Afef Feki

Veronique Capdevielle

Daejung Yoon

Fahad Syed Muhammad

An apparatus, method and computer program are disclosed. The apparatus may include circuitry configured for receiving from a target device, at a first time instance, a set of first measurement data associated with each of a plurality of base stations and determining a first position of the target device based on the received first sets of measurement data. The circuitry may also receive from the target device, at each of one or more subsequent time instances, a second set of measurement data associated with one, or each of a smaller number, of the base stations and determining, at each of the one or more subsequent time instances, a respective position of the target device based on the position determined at a previous time instance and the second set of measurement data.

Position determination

An apparatus, method and computer program is described including: obtaining first channel measurement data for a primary component carrier used for communications between a device and a network node of a mobile communication system; providing the first channel measurement data as an input to an algorithm to obtain a first embedding output; comparing the first embedding output with embedding outputs of a plurality of reference channel measurement data input to said algorithm to identify a closest reference channel measurement data to the first channel measurement data; identifying a set of reference channel information associated with the identified closest reference channel measurement data, wherein the identified set of reference channel information includes a carrier aggregation policy defining one or more secondary communication channels; and setting a carrier aggregation policy for the primary component carrier in accordance with the carrier aggregation policy.

Apparatuses, methods and computer program using channel measurement data for a primary component carrier

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

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.

Training in communication systems

Embodiments can include receiving transmissions over a channel at a receiver of a MIMO communication system; the transmissions are received from a plurality of transmitters in communication with the receiver. Each communication is a separate communication having a modulation and coding scheme. An estimate of the channel is converted into an estimate, for each of a plurality of transmitters, of a bit-metric decoding rate for each of a plurality of MIMO detectors. The estimated bit-metric decoding rate for each of the transmitters for each of the MIMO detectors is converted into an estimated bit error rate for each of the MIMO detectors for each of the transmitters. One of the MIMO detectors is selected for use in generating log-likelihood ratios for bits transmitted from the transmitters to the receiver. The one of the MIMO detectors is selected based on the estimated bit-error rates and a target block error rate.

Growing community of inventors

Orsay, France

Pavan Koteshwar Srinath