17935046. MULTI-DIMENSIONAL GEOMETRIC WIRELESS CHANNEL RENDERING USING MACHINE LEARNING MODELS simplified abstract (QUALCOMM Incorporated)
Contents
- 1 MULTI-DIMENSIONAL GEOMETRIC WIRELESS CHANNEL RENDERING USING MACHINE LEARNING MODELS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MULTI-DIMENSIONAL GEOMETRIC WIRELESS CHANNEL RENDERING USING MACHINE LEARNING MODELS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MULTI-DIMENSIONAL GEOMETRIC WIRELESS CHANNEL RENDERING USING MACHINE LEARNING MODELS
Organization Name
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 17935046 titled 'MULTI-DIMENSIONAL GEOMETRIC WIRELESS CHANNEL RENDERING USING MACHINE LEARNING MODELS
Simplified Explanation
The present disclosure provides techniques and apparatus for training and using machine learning models to estimate a layout of a spatial area. An example method involves estimating a representation of a channel using a machine learning model trained to generate the estimated representation of the channel based on the location of a transmitter, the location of a receiver, and a three-dimensional representation of the spatial environment. Actions are then taken based on the estimated representation of the channel.
- Machine learning models used to estimate layout of spatial areas
- Estimation of channel representation based on transmitter and receiver locations
- Actions taken based on estimated channel representation
Potential Applications
The technology could be applied in various industries such as telecommunications, urban planning, and indoor navigation systems.
Problems Solved
This technology solves the problem of accurately estimating layouts of spatial areas without the need for manual measurements or surveys.
Benefits
The benefits of this technology include improved efficiency in layout estimation, cost savings, and enhanced accuracy in spatial planning.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of advanced indoor mapping and navigation systems for large buildings or complexes.
Possible Prior Art
Prior art may include traditional methods of surveying and mapping spatial areas, which can be time-consuming and costly.
Unanswered Questions
How does this technology compare to traditional surveying methods in terms of accuracy and cost-effectiveness?
This article does not provide a direct comparison between this technology and traditional surveying methods.
What are the limitations of using machine learning models for estimating spatial layouts?
The article does not address any potential limitations or challenges associated with using machine learning models for estimating spatial layouts.
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.
