View source for 18048568. APPLYING WEIGHTED AVERAGING TO MEASUREMENTS ASSOCIATED WITH REFERENCE SIGNALS simplified abstract (QUALCOMM Incorporated)

=APPLYING WEIGHTED AVERAGING TO MEASUREMENTS ASSOCIATED WITH REFERENCE SIGNALS=


==Organization Name==

[[:Category:QUALCOMM Incorporated|QUALCOMM Incorporated]]


[[Category:QUALCOMM Incorporated]]

==Inventor(s)==

[[:Category:Tianyang Bai of Somerville NJ (US)|Tianyang Bai of Somerville NJ (US)]][[Category:Tianyang Bai of Somerville NJ (US)]]

[[:Category:Yan Zhou of San Diego CA (US)|Yan Zhou of San Diego CA (US)]][[Category:Yan Zhou of San Diego CA (US)]]

[[:Category:Hua Wang of Basking Ridge NJ (US)|Hua Wang of Basking Ridge NJ (US)]][[Category:Hua Wang of Basking Ridge NJ (US)]]

[[:Category:Junyi Li of Fairless Hills PA (US)|Junyi Li of Fairless Hills PA (US)]][[Category:Junyi Li of Fairless Hills PA (US)]]

[[:Category:Tao Luo of San Diego CA (US)|Tao Luo of San Diego CA (US)]][[Category:Tao Luo of San Diego CA (US)]]

==APPLYING WEIGHTED AVERAGING TO MEASUREMENTS ASSOCIATED WITH REFERENCE SIGNALS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18048568 titled 'APPLYING WEIGHTED AVERAGING TO MEASUREMENTS ASSOCIATED WITH REFERENCE SIGNALS

==Simplified Explanation==

The present disclosure relates to wireless communication and a method for applying weighted averaging to layer 1 reference signal received power (RSRP) measurements in order to improve beam prediction using machine learning models.

* User equipment (UE) receives a configuration from a network node for applying weighted averaging to L1 RSRP measurements.
* The UE receives a plurality of quasi-co-located reference signals from the network node during a period of time.
* The UE calculates weighted averaged L1 RSRP measurements for the plurality of reference signals based on the configuration.
* The weighted averaged L1 RSRP measurements can be used as input to a machine learning model for beam prediction.

== Potential Applications ==
This technology could be applied in:
* 5G networks for improving beam prediction accuracy.
* IoT devices for optimizing signal reception in crowded environments.

== Problems Solved ==
This technology solves the problem of inaccurate beam prediction in wireless communication systems by utilizing weighted averaging of RSRP measurements.

== Benefits ==
The benefits of this technology include:
* Enhanced beam prediction accuracy.
* Improved signal reception in challenging network conditions.

== Potential Commercial Applications ==
Potential commercial applications of this technology include:
* Telecom companies for optimizing network performance.
* IoT device manufacturers for improving connectivity in various environments.

== Possible Prior Art ==
One possible prior art could be the use of machine learning models for beam prediction in wireless communication systems. However, the specific application of weighted averaging to RSRP measurements for enhancing beam prediction accuracy may be a novel aspect of this technology.

=== Unanswered Questions ===

=== How does this technology impact battery life of user equipment? ===
This article does not address the potential impact of applying weighted averaging to RSRP measurements on the battery life of user equipment. 

=== What are the potential limitations of using machine learning models for beam prediction? ===
This article does not discuss any potential limitations or challenges associated with using machine learning models for beam prediction in wireless communication systems.


==Original Abstract Submitted==

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive, from a network node, a configuration for applying weighted averaging to layer 1 (L1) reference signal received power (RSRP) measurements. The UE may receive, from the network node, a plurality of reference signals during a period of time, the plurality of reference signals being quasi-co-located with each other. The UE may obtain weighted averaged L1 RSRP measurements associated with the plurality of reference signals based at least in part on the configuration, the weighted averaged L1 RSRP measurements being available as input to a machine learning (ML) model for beam prediction. Numerous other aspects are described.



[[Category:H04W24/08]]
[[Category:H04B17/318]]
[[Category:H04W24/10]]
[[Category:H04W72/04]]