Channel Equalizer Coefficient Selection for Faster Kalman Convergence
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing channel equalization methods in digital communication systems face challenges in quickly converging coefficients, especially in multi-path environments, due to the high computational requirements of algorithms like the Kalman algorithm, which are not suitable for real-time data transmission.
Innovation Solution
A method and apparatus that utilize a filtering circuit with adjustable coefficients, updated using a modified Kalman algorithm, which compares coefficients to a threshold to reduce the number of filter cells needing updates, thereby reducing computational load and increasing convergence speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the Kalman algorithm is used to achieve fast coefficient convergence, then the convergence speed is improved, but the computational load and power consumption increase substantially
Solution Approach 1:
The patent segments the filter cells into two groups: active filter cells and inactive filter cells. The coefficient updating circuit selectively updates coefficients only in active filter cells during the initial period, while keeping inactive filter cells dormant. This segmentation reduces the overall computational load and power consumption while maintaining fast convergence in the active cells using the Kalman algorithm.
Solution Approach 2:
The patent applies partial action by updating coefficients in only a subset of filter cells (active cells) rather than all filter cells. The coefficient updating circuit determines which cells are active based on signal characteristics and updates only those cells during specific time periods, thereby reducing computational effort while achieving sufficient convergence for system operation.
2Device complexity
If the least mean square algorithm is used to reduce computational complexity, then the device complexity is reduced, but the coefficient convergence speed decreases
Solution Approach 1:
The patent dynamically switches between different update strategies based on system state and time. During the initial period when fast convergence is critical, the system uses the Kalman algorithm in active filter cells. After the initial period, the system transitions to updating only selected cells based on signal characteristics, thereby adapting the computational complexity to the current operational requirements.
Solution Approach 2:
The patent implements periodic action by dividing the operation into distinct time periods: an initial period where coefficient updating is performed in active filter cells to achieve fast convergence, and a subsequent period where updating is performed selectively based on signal characteristics. This periodic approach balances convergence speed and computational complexity at different stages of operation.
Data Source
AI summary
A method and apparatus for performing channel equalization in a communication system may potentially reduce power consumption in channel equalizers of communication systems. A filtering circuit filters a received data sequence as a plurality of data values to be stored in a plurality of filter cells. Each filter cell may store at least one data value and may contain a coefficient related to the stored data value. A coefficient updating circuit may update the coefficients based on at least one parameter, and may compare the updated coefficients to a threshold. Based on the comparison, filter cells of selected coefficients may be selected for restoring the received data sequence to its original state.