Equalizer Determination Method for Power Line Communication
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Solution Overview
Problem
In power line communication systems, the high computational complexity of determining equalizers, especially when using lattice reduction, makes it impractical for real-time applications, especially in impulsive noise environments, as it requires frequent recalculations of the entire transformation matrix and equalizer due to channel changes.
Innovation Solution
A method that selectively chooses between two determination methods for equalizers based on the change in channel conditions, allowing for partial or no recalculations when changes are minor, and uses pre-processing to reduce impulsive noise, thereby reducing computational effort and maintaining performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lattice reduction is used to improve equalizer performance, then detection accuracy approaches maximum likelihood, but computational complexity becomes prohibitively high for real-time applications
Solution Approach 1:
The system dynamically adjusts the equalizer determination strategy based on channel condition changes. When channel conditions are stable, the system reuses previously determined equalizers to minimize computation. When significant changes are detected, the system recalculates equalizers using lattice reduction. This dynamic adaptation allows the system to achieve near-ML performance when needed while maintaining low computational complexity during stable periods, directly resolving the contradiction between detection accuracy and computational complexity.
Solution Approach 2:
The invention changes the parameter of equalizer update frequency based on channel condition monitoring. Instead of continuously recalculating equalizers (high complexity) or never updating them (low accuracy), the system monitors channel conditions and adjusts the equalizer determination parameter accordingly. This selective update strategy enables the system to maintain high detection accuracy through periodic lattice reduction while avoiding the prohibitive computational cost of continuous recalibration.
2Reliability
If equalizer recalculations are performed frequently to adapt to channel changes, then detection performance is maintained, but computational burden increases significantly
Solution Approach 1:
The system implements a feedback mechanism where channel conditions are continuously monitored and compared against threshold values. This feedback loop enables intelligent decision-making about when equalizer recalibration is necessary. The feedback-driven approach ensures that computational resources are allocated only when performance degradation is detected, maintaining reliable detection performance while minimizing unnecessary computational burden and energy consumption.
Solution Approach 2:
The system applies partial action by performing equalizer recalibration only when channel changes exceed a certain threshold, rather than continuously or excessively. This selective partial recalibration maintains adequate detection performance by updating equalizers only when necessary, avoiding the excessive computational burden of frequent full recalculations while still adapting to significant channel variations.
3Measurement precision
If complete equalizer recalculation is performed every time, then equalizer accuracy is maximized, but processing time and complexity increase
Solution Approach 1:
The system segments the equalizer determination process into conditional branches: a fast path that reuses existing equalizers when channel conditions are stable, and a slow path that performs complete lattice reduction recalibration when changes are significant. This segmentation allows the system to achieve high equalizer accuracy only when necessary, while reducing processing time through the efficient reuse of previously determined equalizers during stable channel conditions.
Solution Approach 2:
The system performs preliminary monitoring of channel conditions to determine whether complete equalizer recalculation is necessary. This preliminary assessment action allows the system to avoid unnecessary full recalculations by identifying stable channel conditions in advance. When changes are detected, the system then performs the complete recalculation to maximize equalizer accuracy, thus optimizing the balance between processing time and equalizer precision.
Data Source
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AI summary
A method for determining an equalizer for use in a data path at a receiver side of a wired communication during a time period, the method comprising the steps of: determining channel conditions of the wired communication during the time period; determining whether the determined channel conditions have changed compared to channel conditions during a previous time period; making a selection between at least a first and a second determination method to determine an equalizer based on the determined change of the channel conditions; and determining the equalizer based on the selected method.