Qualcomm incorporated (20240112009). MULTI-DIMENSIONAL GEOMETRIC WIRELESS CHANNEL RENDERING USING MACHINE LEARNING MODELS simplified abstract

MULTI-DIMENSIONAL GEOMETRIC WIRELESS CHANNEL RENDERING USING MACHINE LEARNING MODELS

Organization Name

qualcomm incorporated

Inventor(s)

Tribhuvanesh Orekondy of Biel (CH)

Arash Behboodi of Amsterdam (NL)

Kumar Pratik of Amsterdam (NL)

Joseph Binamira Soriaga of San Diego CA (US)

Shreya Kadambi of San Diego CA (US)

MULTI-DIMENSIONAL GEOMETRIC WIRELESS CHANNEL RENDERING USING MACHINE LEARNING MODELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240112009 titled 'MULTI-DIMENSIONAL GEOMETRIC WIRELESS CHANNEL RENDERING USING MACHINE LEARNING MODELS

Simplified Explanation

The present disclosure describes techniques for training and using machine learning models to estimate a layout of a spatial area. The method involves estimating a representation of a channel using a machine learning model trained based on the location of a transmitter, the location of a receiver, and a three-dimensional representation of the spatial environment.

• Machine learning model trained to estimate channel representation
• Based on transmitter and receiver locations in a spatial environment
• Three-dimensional representation of the spatial environment used
• Actions taken based on estimated channel representation

Potential Applications

This technology could be applied in:

• Wireless communication systems
• Indoor navigation systems
• Autonomous vehicles

Problems Solved

This technology helps in:

• Improving signal strength and quality in wireless communication
• Enhancing accuracy in indoor positioning systems
• Optimizing navigation routes for autonomous vehicles

Benefits

The benefits of this technology include:

• Increased efficiency in wireless communication
• Enhanced user experience in indoor navigation
• Improved safety and performance of autonomous vehicles

Potential Commercial Applications

Potential commercial applications of this technology include:

• Telecommunication companies for optimizing network performance
• Indoor mapping and navigation companies for improving user experience
• Autonomous vehicle manufacturers for enhancing driving capabilities

Possible Prior Art

One possible prior art could be the use of traditional signal processing techniques for estimating channel characteristics in wireless communication systems.

How does this technology compare to traditional signal processing techniques for estimating channel characteristics in wireless communication systems?

This technology utilizes machine learning models to estimate channel representations, which may offer advantages in terms of accuracy and efficiency compared to traditional signal processing techniques.

What are the limitations of using machine learning models for estimating channel representations in a spatial environment?

One limitation could be the need for extensive training data to ensure the accuracy and reliability of the estimated channel representations. Additionally, the complexity of the spatial environment may pose challenges in accurately capturing all relevant factors affecting the channel.

Original Abstract Submitted

certain aspects of the present disclosure provide techniques and apparatus for training and using machine learning models to estimate a layout of a spatial area. an example method generally includes estimating a representation of a channel using a machine learning model trained to generate the estimated representation of the channel based on a location of a transmitter in a spatial environment, a location of a receiver in the spatial environment, and a three-dimensional representation of the spatial environment. one or more actions are taken based on the estimated representation of the channel.