Adaptive FTM Ranging Parameters for WiFi Location Accuracy
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Solution Overview
Problem
As wireless networks become denser, the increase in FTM exchanges between access points and devices leads to congestion, which negatively impacts the accuracy of location determination in WiFi networks.
Innovation Solution
Implementing artificial intelligence through machine learning models to predict ranging accuracies and set FTM ranging parameters, balancing accuracy and congestion by selecting appropriate bandwidth and burst structures for FTM exchanges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of FTM exchanges is increased to improve location determination accuracy, then measurement precision improves, but network congestion increases
Solution Approach 1:
The patent applies dynamics by making the FTM exchange parameters adjustable and adaptive rather than fixed. The system dynamically modifies ranging parameters such as bandwidth and burst structure based on real-time network conditions, allowing the network to optimize between accuracy and congestion differentially across time and space
Solution Approach 2:
The patent implements local quality by allowing different FTM exchanges to use different ranging parameters based on their specific network conditions. Each FTM exchange can be customized with appropriate bandwidth and burst structure settings according to local network congestion levels and accuracy requirements, rather than applying uniform parameters network-wide
Solution Approach 3:
The patent directly applies parameter changes by modifying FTM ranging parameters (bandwidth, burst structure) to resolve the contradiction. By changing these parameters, the system can increase accuracy when needed while controlling congestion through selective parameter adjustment in different network conditions
2Object-generated harmful factors
If access points reject or override FTM requests to reduce network congestion, then network congestion decreases, but location determination accuracy deteriorates
Solution Approach 1:
Instead of simply rejecting FTM requests, the system changes the ranging parameters of accepted requests. By modifying bandwidth and burst structure parameters, the system can accommodate more FTM exchanges while controlling congestion through parameter optimization rather than outright rejection
Solution Approach 2:
The system dynamically evaluates FTM requests and applies different parameter settings or acceptance decisions based on current network conditions. This dynamic approach allows the system to maintain accuracy for critical measurements while controlling congestion through adaptive parameter adjustment rather than static rejection rules
Data Source
AI summary
The present disclosure describes wireless networks (e.g., WiFi networks) that use machine learning to determine FTM ranging parameters for FTM exchanges. An apparatus includes one or more memories and one or more processors communicatively coupled to the one or more memories. A combination of the one or more processors predicts a ranging accuracy for a device by applying a machine learning model to a request from the device to perform an FTM exchange, determines, based on the ranging accuracy, an FTM ranging parameter for the FTM exchange, and performs the FTM exchange based on the FTM ranging parameter to determine a location of the device.