公开(公告)号：US11632188B1

公开(公告)日：2023-04-18

申请号：US17953828

申请日：2022-09-27

Applicant: NOKIA SOLUTIONS AND NETWORKS OY

Inventor： Jafar Mohammadi ,  Paolo Baracca ,  Mahmoud Ramezani-Mayiami ,  Silvio Mandelli ,  Andreas Weber

IPC: H04W4/00 ,  H04L1/00 ,  H04W24/02

Abstract: A modulation and coding scheme for a transmission to an apparatus may be selected by at least sampling a posteriori probability distribution that has been calculated using a first probability distribution and a second probability distribution. The first probability distribution is calculated using at least a newest first feedback and a plurality of older first feedbacks, a first feedback indicating channel quality. The second probability distribution is calculated using at least a newest second feedback and a plurality of older second feedbacks, a second feedback indicating a success or failure of an earlier transmission transmitted from the apparatus.

公开(公告)号：US20240039766A1

公开(公告)日：2024-02-01

申请号：US18555521

申请日：2021-04-16

Applicant: Nokia Solutions and Networks Oy

Inventor： Gerhard Schreiber ,  Jafar Mohammadi ,  Thorsten Wild

IPC: H04L25/02

CPC classification number: H04L25/021 ,  H04L25/0254

Abstract: An apparatus, comprising at least one processor and at least one memory storing instructions, the at least one memory and the instructions configured to, with the at least one processor, cause a receiver to perform a correlation processing of a first signal received by the receiver and/or of a second signal which can be derived from the first signal, and to at least temporarily modify an output signal of the correlation processing using at least one neural network, wherein a modified output signal may be obtained.

公开(公告)号：US20240364562A1

公开(公告)日：2024-10-31

申请号：US18644319

申请日：2024-04-24

Applicant: Nokia Solutions and Networks Oy

Inventor： Jafar Mohammadi ,  Milan ZIVKOVIC

IPC: H04L25/02

CPC classification number: H04L25/0204 ,  H04L25/0254

Abstract: A technical solution is provided, which enhances the performance of Machine Learning (ML)-based channel estimation by using two different pilot sequences. More specifically, a first pilot sequence (e.g., a sequence of Demodulation Reference Signals (DMRSs)) is designed to track a change in a wireless communication channel in a frequency domain, while a second pilot sequence (e.g., a sequence of Phase-Tracking RSs (PTRSs)) is designed to track a change in the wireless communication channel in a time domain. By using the first and second pilot sequences thus designed, raw channel estimates in the frequency and time domains, respectively, are obtained on a receiving side. Then, the raw channel estimates are fed to a ML model that is configured to predict a full (time-frequency) channel estimate based thereon. The full channel estimate may be subsequently used in a data decoding algorithm executed on the receiving side.