WLAN Throughput Prediction Using Uplink Signal Metrics
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Solution Overview
Problem
Existing WLAN throughput prediction methods are inadequate as they only account for a subset of channel impairments, leading to significant prediction errors due to unconsidered variations in channel conditions.
Innovation Solution
A method that predicts downlink data rate for WLAN transmissions by using signal qualities of uplink transmissions or a default signal quality value, weighted by a metric accounting for channel conditions, such as average UE throughput and signal qualities of multiple UEs, to determine the predicted downlink throughput for controlling the basic service set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a simple mathematical function mapping RSSI to predicted data rate is used, then the prediction method is simple and easy to implement, but the prediction accuracy is poor due to not accounting for channel impairments
Solution Approach 1:
The patent transforms the single-parameter RSSI-based prediction into a multi-parameter prediction model that incorporates signal qualities from multiple uplink transmissions and statistical measurements of downstream transmissions. This changes the prediction from using one parameter (RSSI) to using multiple parameters (signal qualities of multiple UEs, average throughput, channel condition metrics) to improve accuracy while maintaining implementation feasibility through systematic data collection and processing
Solution Approach 2:
The patent implements feedback mechanisms by using statistical measurements of recent downstream transmissions and signal qualities from multiple UEs to continuously refine the throughput prediction. The system collects feedback data from actual channel conditions and uses this feedback to adjust and improve prediction accuracy, creating a closed-loop system that adapts to changing channel conditions
2Measurement precision
If statistical measurements of recent downstream transmissions are incorporated, then the prediction accuracy improves by accounting for channel impairments, but the complexity of the prediction method increases
Solution Approach 1:
The patent creates a universal prediction framework that can handle multiple types of channel impairments simultaneously through a single integrated model. The method uses a standardized approach that works across different WLAN conditions and scenarios, making the complex prediction process universally applicable rather than requiring separate models for different impairment types
Solution Approach 2:
The patent segments the complex prediction task into distinct components: collecting signal qualities from multiple uplink transmissions, calculating statistical measurements of downstream transmissions, determining channel condition metrics, and combining these elements in a weighted prediction formula. This segmentation makes the overall complex process more manageable and implementable by breaking it into discrete, executable steps
Data Source
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AI summary
According to one aspect of the present disclosure, a method (200) is implemented by a network node in relation to a basic service set (BSS) that includes an access point providing a Wireless Local Area Network (WLAN). The network node predicts (202) a downlink data rate for downlink transmissions from the access point to a particular UE based on either signal qualities of a first set of uplink transmissions which are sent from the particular UE to the access point or a default signal quality value. The UE is either already part of the BSS or is being evaluated for admission to the BSS. The predicted downlink data rate is weighted (204) by a metric that accounts for channel conditions of the BSS to determine a predicted downlink throughput for downlink transmissions from the access point to the particular UE. The predicted downlink throughput is used (206) for controlling the BSS.