The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 21, 2025

Filed:

Apr. 04, 2022
Applicant:

Qualcomm Incorporated, San Diego, CA (US);

Inventors:

Roohollah Amiri, San Diego, CA (US);

Srinivas Yerramalli, San Diego, CA (US);

Taesang Yoo, San Diego, CA (US);

Mohammed Ali Mohammed Hirzallah, San Diego, CA (US);

Marwen Zorgui, San Diego, CA (US);

Mohammad Tarek Fahim, San Diego, CA (US);

Rajat Prakash, San Diego, CA (US);

Xiaoxia Zhang, San Diego, CA (US);

Assignee:

QUALCOMM Incorporated, San Diego, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04B 17/391 (2015.01); H04B 17/309 (2015.01); H04W 4/02 (2018.01); H04W 24/02 (2009.01); H04W 24/10 (2009.01); H04W 64/00 (2009.01);
U.S. Cl.
CPC ...
H04B 17/3913 (2015.01); H04B 17/309 (2015.01); H04W 4/023 (2013.01); H04W 24/02 (2013.01); H04W 24/10 (2013.01); H04W 64/00 (2013.01);
Abstract

Methods and apparatus for predicting wireless measurements based on virtual access points is described. In some embodiments, a location of a user equipment (UE) may be obtained, and given the location of the UE, an output may be generated using a machine learning model, the output including one or more predicted wireless measurements. The output may be indicative of a wireless channel in a multipath environment. In some variants, the machine learning model may have been trained by obtaining a training dataset including multipath components data and ground truth locations of a wireless device, and performing an optimization with respect to the multipath components data and the ground truth locations. In some implementations, a training output including a predicted multipath component may be produced during the training, and the optimization may include an iterative minimization of an error between at least the predicted multipath component and a labeled multipath component.


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