Qualcomm incorporated (20240129759). MACHINE LEARNING MODEL REPORTING, FALLBACK, AND UPDATING FOR WIRELESS COMMUNICATIONS simplified abstract

MACHINE LEARNING MODEL REPORTING, FALLBACK, AND UPDATING FOR WIRELESS COMMUNICATIONS

Organization Name

qualcomm incorporated

Inventor(s)

Yuwei Ren of Beijing (CN)

Ruiming Zheng of Beijing (CN)

Xipeng Zhu of San Diego CA (US)

Chenxi Hao of Beijing (CN)

Shankar Krishnan of San Diego CA (US)

Yu Zhang of San Diego CA (US)

Huilin Xu of Temecula CA (US)

Hao Xu of Beijing (CN)

Yin Huang of Beijing (CN)

Taesang Yoo of San Diego CA (US)

MACHINE LEARNING MODEL REPORTING, FALLBACK, AND UPDATING FOR WIRELESS COMMUNICATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240129759 titled 'MACHINE LEARNING MODEL REPORTING, FALLBACK, AND UPDATING FOR WIRELESS COMMUNICATIONS

Simplified Explanation

The abstract describes methods, systems, and devices for wireless communications that utilize machine learning models to support communication. User equipment (UE) may download ML model information from a network, which can configure status reporting and fallback procedures for the ML model. The UE may transmit status reports to a base station based on triggers and may fallback from using the ML model to a second mode based on a trigger. To restore operation using a downloaded ML model, the UE may download an updated model or receive iterative updates.

• Wireless communications system utilizing machine learning models
• User equipment downloads ML model information from a network
• Configuration of status reporting and fallback procedures for ML model
• UE transmits status reports to base station based on triggers
• Fallback from ML model to second mode based on trigger
• Restoration of operation using downloaded ML model through updates

Potential Applications

The technology described in the patent application can be applied in various wireless communication systems, such as 5G networks, IoT devices, and smart city infrastructure.

Problems Solved

This technology addresses the need for efficient and adaptive wireless communication systems that can utilize machine learning models to improve performance and reliability.

Benefits

The use of machine learning models in wireless communications systems can lead to enhanced network optimization, increased data throughput, and improved user experience.

Potential Commercial Applications

The technology can be implemented in telecommunications companies, network equipment manufacturers, and IoT device manufacturers to enhance the performance and reliability of wireless communication systems.

Possible Prior Art

One possible prior art for this technology could be the use of machine learning models in other communication systems, such as in data analytics or network optimization.

How does the system handle security and privacy concerns related to the use of machine learning models in wireless communications?

The abstract does not provide information on how the system addresses security and privacy concerns related to the use of machine learning models.

What are the potential limitations or challenges in implementing this technology in real-world wireless communication systems?

The abstract does not mention any potential limitations or challenges in implementing this technology in real-world wireless communication systems.

Original Abstract Submitted

methods, systems, and devices for wireless communications are described. in some systems, devices use machine learning (ml) models to support wireless communications. for example, a user equipment (ue) may download ml model information from a network to determine an ml model. the network may additionally configure a status reporting procedure, a fallback procedure, or both for the ml model. in some examples, based on a configuration, the ue may transmit a status report to a base station according to a reporting periodicity, a ue-based trigger, a network-based trigger, or some combination thereof. additionally or alternatively, the ue may determine to fallback from operating using the ml model to operating in a second mode based on a fallback trigger. in some examples, to restore operating using a downloaded ml model, the ue may download an updated ml model or receive iterative updates to a previously downloaded ml model.