View source for Qualcomm incorporated (20240137789). APPLYING WEIGHTED AVERAGING TO MEASUREMENTS ASSOCIATED WITH REFERENCE SIGNALS simplified abstract

=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 20240137789 titled 'APPLYING WEIGHTED AVERAGING TO MEASUREMENTS ASSOCIATED WITH REFERENCE SIGNALS

==Simplified Explanation==

The present disclosure relates to wireless communication and involves a method for applying weighted averaging to layer 1 reference signal received power measurements in a user equipment (UE) for beam prediction using machine learning.

* The UE receives a configuration from a network node for applying weighted averaging to layer 1 reference signal received power (RSRP) measurements.
* The UE receives a plurality of quasi-co-located reference signals from the network node during a period of time.
* The UE obtains weighted averaged layer 1 RSRP measurements associated with the plurality of reference signals based on the configuration.
* The weighted averaged layer 1 RSRP measurements can be used as input to a machine learning model for beam prediction.

== Potential Applications ==
This technology can be applied in:
* Wireless communication systems
* Beamforming techniques
* Machine learning models for signal prediction

== Problems Solved ==
This technology helps in:
* Improving beam prediction accuracy
* Enhancing signal strength measurements
* Optimizing wireless communication performance

== Benefits ==
The benefits of this technology include:
* Enhanced network efficiency
* Improved signal quality
* Better user experience in wireless communication

== Potential Commercial Applications ==
Potential commercial applications include:
* Telecommunication companies
* Network equipment manufacturers
* Wireless technology providers

== Possible Prior Art ==
One possible prior art could be:
* Previous methods of beam prediction in wireless communication systems

=== Unanswered Questions ===

=== How does this technology impact battery life in user equipment (UE)? ===
This article does not address the potential impact of this technology on the battery life of the UE. It would be interesting to know if the weighted averaging process affects power consumption in the device.

=== What are the limitations of using machine learning models for beam prediction in wireless communication systems? ===
The article does not discuss any limitations or challenges associated with using machine learning models for beam prediction. It would be valuable to understand any potential drawbacks or constraints in implementing such models in real-world scenarios.


==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.



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[[Category:H04B17/318]]
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