US 12,349,100 B2
Similarity learning for crowd-sourced positioning
Sooryanarayanan Gopalakrishnan, San Diego, CA (US); Jay Kumar Sundararajan, San Diego, CA (US); Taesang Yoo, San Diego, CA (US); Naga Bhushan, San Diego, CA (US); Guttorm Ringstad Opshaug, Redwood City, CA (US); Grant Marshall, Campbell, CA (US); Chandrakant Mehta, Cupertino, CA (US); and Zongjun Qi, Saratoga, CA (US)
Assigned to QUALCOMM Incorporated, San Diego, CA (US)
Filed by QUALCOMM Incorporated, San Diego, CA (US)
Filed on Sep. 14, 2022, as Appl. No. 17/932,174.
Prior Publication US 2024/0089905 A1, Mar. 14, 2024
Int. Cl. H04W 64/00 (2009.01); G01S 5/02 (2010.01); H04W 24/08 (2009.01)
CPC H04W 64/003 (2013.01) [G01S 5/02526 (2020.05); H04W 24/08 (2013.01)] 30 Claims
OG exemplary drawing
 
1. An apparatus for wireless communication at a network entity, comprising:
memory; and
at least one processor coupled to the memory and, based at least in part on information stored in the memory, the at least one processor is configured to:
receive, from a user equipment (UE), a first set of measurements associated with at least one cell; and
perform a position estimation of the UE based on at least one of the first set of measurements associated with the at least one cell, a second set of measurements for each of a set of reference UEs, or a location of each of the set of reference UEs via at least one machine learning (ML) model, wherein the UE and the set of reference UEs include at least one common cell, wherein the at least one ML model is capable of identifying the at least one common cell between the UE and the set of reference UEs, and wherein an output of the at least one ML model includes a similarity score between the UE and the set of reference UEs, wherein the similarity score is on a per-cell basis.