Qualcomm Incorporated

Zongjun Qi

Naga Bhushan

Taesang Yoo

Jay Kumar Sundararajan

Guttorm Ringstad Opshaug

Grant Alexander Marshall

Chandrakant Mehta

Sooryanarayanan Gopalakrishnan

Aspects presented herein may enhance the accuracy and/or latency of UE positioning based on crowd-sourcing, where a network entity may compute a position estimate of a UE based on neighbor-cell scan data from the UE and one or more reference UEs. In one aspect, a network entity receives a first set of measurements associated with at least one cell from a UE. The network entity performs 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 an ML model, where the UE and the set of reference UEs include at least one common cell.

Similarity learning for crowd-sourced positioning

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Saratoga, CA, United States of America

Zongjun Qi