17930644. WIRELESS RECEIVE SIGNAL STRENGTH INDICATOR (RSSI)-BASED POSITIONING simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)
Contents
WIRELESS RECEIVE SIGNAL STRENGTH INDICATOR (RSSI)-BASED POSITIONING
Organization Name
Inventor(s)
Tianwei Xing of Santa Clara CA (US)
Wenjun Jiang of San Jose CA (US)
WIRELESS RECEIVE SIGNAL STRENGTH INDICATOR (RSSI)-BASED POSITIONING - A simplified explanation of the abstract
This abstract first appeared for US patent application 17930644 titled 'WIRELESS RECEIVE SIGNAL STRENGTH INDICATOR (RSSI)-BASED POSITIONING
Simplified Explanation
The patent application describes a method for generating missing signal strength values in wireless fingerprint data using machine learning.
- The method receives wireless fingerprint data that identifies multiple locations within a specified area and includes signal strength values associated with wireless signals received from multiple transmitters.
- However, the wireless fingerprint data may have missing signal strength values for some transmitters at certain locations.
- To address this, the method generates a training dataset by adding filler signal strength values in place of the missing values.
- A machine learning model is then trained using this dataset, which takes a specified location as input and generates predicted signal strength values as outputs.
- The trained model can be used to generate additional signal strength values, which can be used to replace the missing values in the wireless fingerprint data.
Potential applications of this technology:
- Wireless network optimization: The method can be used to improve the accuracy of wireless network planning and optimization by generating missing signal strength values in wireless fingerprint data.
- Indoor positioning systems: By generating missing signal strength values, the method can enhance the accuracy and reliability of indoor positioning systems that rely on wireless signals.
- Network troubleshooting: The technology can assist in troubleshooting network issues by providing more complete and accurate wireless fingerprint data.
Problems solved by this technology:
- Missing signal strength values: The method addresses the problem of missing signal strength values in wireless fingerprint data, which can hinder accurate wireless network planning and optimization.
- Incomplete data: By generating missing signal strength values, the method helps to ensure that the wireless fingerprint data is more complete and reliable for various applications.
Benefits of this technology:
- Improved accuracy: By generating missing signal strength values, the method enhances the accuracy of wireless fingerprint data, leading to more accurate wireless network planning, indoor positioning, and network troubleshooting.
- Time and cost savings: The use of machine learning to generate missing signal strength values eliminates the need for manual data collection or expensive equipment, resulting in time and cost savings for network operators and service providers.
Original Abstract Submitted
A method includes receiving wireless fingerprint data identifying multiple locations within a specified area and, for each location, one or more signal strength values associated with wireless signals received from one or more of multiple wireless transmitters. The wireless fingerprint data is missing signal strength values for one or more transmitters at one or more specific locations. The method also includes generating a training dataset by adding filler signal strength values in place of at least some missing values. The method further includes training a machine learning model using the training dataset. The model is trained to receive a specified location as input and generate predicted signal strength values as outputs. In addition, the method includes using the trained model to generate additional signal strength values. At least some additional signal strength values are to be used in place of at least a portion of the missing values.
