Multirate Predictor for Radio Network Node Channel Gain
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Solution Overview
Problem
Current wireless communication networks face inefficiencies in channel quality estimation and link adaptation due to non-uniform sampling rates and the Doppler effect, leading to outdated channel information and reduced network performance, especially for fast-moving devices.
Innovation Solution
A multirate predictor is used in a radio network node to predict channel gain by employing first and second sampling descriptors, allowing for linear prediction and handling varying sampling periods, which enables improved channel estimation and link adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If uniform sampling rate is used for channel estimation, then device complexity is reduced, but measurement precision deteriorates due to outdated channel information for fast-moving devices
Solution Approach 1:
The patent applies dynamics by making the sampling rate adaptive rather than fixed. The sampling rate changes based on detected channel conditions and device mobility patterns. When channel variations are detected or mobility increases, the sampling rate automatically increases to capture current channel state, otherwise it reduces to save resources. This dynamic adjustment resolves the contradiction between maintaining simple uniform sampling and achieving precise measurements for moving devices.
Solution Approach 2:
The patent changes the sampling rate parameter based on channel conditions and mobility detection. Instead of using a constant sampling rate, the system adjusts this parameter dynamically - increasing it when channel variations are detected and decreasing it when conditions are stable. This parameter change allows the system to optimize between measurement precision and device complexity according to actual operational needs.
2Measurement precision
If sampling rate is increased to capture channel variations, then measurement precision is improved, but use of energy increases due to more frequent measurements and processing
Solution Approach 1:
The system dynamically adjusts the sampling rate based on detected channel variations and mobility patterns. Instead of continuously sampling at high rate, the system only increases sampling when necessary - when channel conditions change or device mobility is detected. This dynamic approach ensures measurement precision is maintained when needed while minimizing energy consumption during stable conditions.
Solution Approach 2:
The sampling rate parameter is changed adaptively based on channel conditions rather than being fixed at a high value. The system increases the sampling rate parameter only when channel variations are detected, and reduces it during stable periods. This parameter adjustment directly addresses the energy consumption issue by ensuring high-precision measurements are performed only when necessary.
3Measurement precision
If channel sampling is performed frequently to track Doppler effects, then measurement precision is improved, but loss of time increases due to processing delays
Solution Approach 1:
The patent applies preliminary action by performing channel predictions based on previously collected channel samples. Instead of waiting for frequent new measurements, the system uses historical data to predict current channel state. This preliminary prediction approach provides timely channel information for scheduling decisions without requiring continuous frequent sampling, thus reducing processing time while maintaining tracking accuracy.
Solution Approach 2:
The system creates a predicted copy of the current channel state based on historical samples rather than relying solely on real-time measurements. This predicted channel state copy is used for immediate scheduling decisions, reducing the time loss associated with waiting for new measurements while maintaining sufficient accuracy for practical purposes.
4Measurement precision
If non-uniform sampling is used to adapt to varying channel conditions, then measurement precision is improved, but device complexity increases due to handling multiple sampling rates
Solution Approach 1:
The patent applies universality by designing a prediction system that handles multiple sampling rates through a unified multirate prediction framework. Instead of requiring separate processing systems for different sampling rates, the universal predictor can process channel samples regardless of when they were taken. This multi-functional approach maintains measurement precision under varying sampling conditions while minimizing the increase in device complexity through a single versatile prediction mechanism.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides accurate channel gain prediction across multiple sampling rates, enhancing network capacity and voice quality while reducing computational complexity and prediction errors.
Implementation Method 1
Current wireless communication networks face inefficiencies in channel quality estimation and link adaptation due to non-uniform sampling rates and the Doppler effect, leading to outdated channel information and reduced network performance, especially for fast-moving devices.
Data Source
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AI summary
A method performed by radio network node (12) for enabling channel handling of a channel between a wireless device (10) and the radio network node (12) in a wireless communication network (1). The channel is defined in continuous time and a sampling rate of the channel is non-uniform. The radio network node (12) predicts a channel gain using a first sampling descriptor indicating a first momentary sampling frequency and a second sampling descriptor indicating a second momentary sampling frequency, wherein the first sampling descriptor operates on a different segment of continuous time than the second sampling descriptor. The predicted channel gain enables channel handling such as channel estimation and link adaptation.