Qualcomm incorporated (20240187906). DATA TRANSMISSION CONFIGURATION UTILIZING A STATE INDICATION simplified abstract

DATA TRANSMISSION CONFIGURATION UTILIZING A STATE INDICATION

Organization Name

qualcomm incorporated

Inventor(s)

Ahmed Elshafie of San Diego CA (US)

Yi Huang of San Diego CA (US)

Hwan Joon Kwon of San Diego CA (US)

Krishna Kiran Mukkavilli of San Diego CA (US)

Jay Kumar Sundararajan of San Diego CA (US)

Wei Yang of San Diego CA (US)

DATA TRANSMISSION CONFIGURATION UTILIZING A STATE INDICATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240187906 titled 'DATA TRANSMISSION CONFIGURATION UTILIZING A STATE INDICATION

Simplified Explanation

The patent application describes techniques for configuring data transmission using a machine-learning based algorithm, specifically for ultra-reliable low-latency communication (URLLC) applications. A base station receives feedback from user equipment indicating channel conditions and determines actions based on this feedback using a machine learning algorithm.

• The patent application focuses on configuring data transmission for ultra-reliable low-latency communication (URLLC) applications using machine learning algorithms.
• The base station receives feedback from user equipment regarding channel conditions and uses this information to determine actions for optimizing data transmission.
• Machine learning algorithms are utilized to analyze the feedback and determine the best course of action for improving data transmission in URLLC applications.

Potential Applications

The technology described in the patent application could be applied in various industries and scenarios, including:

• Telecommunications for improving data transmission in ultra-reliable low-latency communication applications.
• Internet of Things (IoT) devices that require reliable and low-latency communication.
• Autonomous vehicles that rely on real-time data transmission for safe operation.

Problems Solved

The technology addresses the following issues:

• Ensuring reliable and low-latency data transmission in URLLC applications.
• Optimizing data transmission based on real-time feedback from user equipment.
• Improving overall performance and efficiency of data transmission in critical communication scenarios.

Benefits

The benefits of this technology include:

• Enhanced reliability and low-latency communication in critical applications.
• Efficient utilization of resources for data transmission optimization.
• Improved overall performance and user experience in URLLC applications.

Potential Commercial Applications

The technology has potential commercial applications in:

• Telecommunication companies offering URLLC services.
• IoT device manufacturers looking to enhance communication capabilities.
• Companies developing autonomous vehicles and other real-time communication systems.

Possible Prior Art

One possible prior art for this technology could be existing machine learning algorithms used in telecommunications for optimizing data transmission based on feedback from user equipment. Additionally, prior research on ultra-reliable low-latency communication systems may provide insights into similar techniques for configuring data transmission.

Original Abstract Submitted

certain aspects of the present disclosure provide techniques for configuring data transmission. aspects relate to determining a data transmission configuration utilizing a machine-learning based algorithm, such as a data transmission configuration for ultra-reliable low-latency communication (urllc) applications. a method that may be performed by a base station (bs) includes receiving a feedback report from a user equipment (ue) including an indication of a first state corresponding to a plurality of channel condition parameters and determining one or more actions based, at least in part, on the first state. the bs may determining the one or more actions utilizing a machine learning algorithm that uses a second state, where the second state is based, at least in part, on the first state.