Data and Management Frame Classification Using PHY Rate and RSSI
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Solution Overview
Problem
Existing wireless network systems struggle to accurately distinguish between data frames and management frames, leading to inaccuracies in determining the activity status of stations, which is crucial for efficient handover management in wireless networks with multiple Access Points.
Innovation Solution
A method and device that utilize signal strength (RSSI) in conjunction with PHY rate to classify frames as data or management frames, employing a mechanism that combines fixed and dynamic adjustments for improved accuracy, and optionally uses a margin to refine the classification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If PHY rate threshold filtering is used to distinguish data frames from management frames, then the classification process is simple, but false conclusions occur due to overlapping PHY rate ranges
Solution Approach 1:
The patent transitions from single-dimensional PHY rate filtering to a two-dimensional classification approach by introducing signal strength (RSSI) as an additional dimension. This allows distinguishing frames based on the combination of PHY rate and signal strength, resolving the ambiguity where management frames with low PHY rates could be mistaken for data frames from distant stations.
Solution Approach 2:
The patent changes the classification parameters from solely PHY rate to a combination of PHY rate and signal strength. By modifying the parameter set used for frame classification, the system achieves more accurate distinction between data and management frames while maintaining operational simplicity through standardized parameter collection.
2Device complexity
If a single representative PHY rate value is maintained per station, then the data collection process is simplified, but the ability to distinguish frame types is lost
Solution Approach 1:
The patent makes the PHY rate and signal strength parameters multi-functional by using them for both traditional throughput calculations and frame type classification simultaneously. This eliminates the need for separate classification mechanisms while preserving the ability to distinguish frame types, as the same collected parameters serve dual purposes.
Solution Approach 2:
The system uses feedback from the classification process to refine future measurements. By continuously monitoring the relationship between PHY rate and signal strength, the system can adapt its classification thresholds and improve its ability to distinguish frame types over time while maintaining simplified data collection.
3Use of energy by moving object
If management frames are transmitted at low PHY rates, then power consumption is reduced, but accurate frame type identification becomes difficult
Solution Approach 1:
The patent introduces signal strength as an intermediary parameter that mediates between PHY rate and frame type identification. This intermediary allows the system to distinguish management frames from data frames by examining the relationship between PHY rate and signal strength, resolving the identification difficulty caused by low PHY rates while preserving the power savings.
Data Source
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AI summary
A device (240) comprising at least one hardware processor (241) obtains (S310) a bandwidth for a communication from a station (210) in a wireless network (200) and a signal strength for the communication from the station (210), and determines (S350, S360) that the communication is a communication related to network maintenance in case the bandwidth is below a first value and an expected bandwidth based on the signal strength is above a second value, and that the communication is a data communication in case the bandwidth is below the first value and the signal strength is below the second value. In case the communication is a data communication, the at least one hardware processor (241) can determine that the station (210) is active.