Decision Feedback Equalizer Error Detection for Burst Errors
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Solution Overview
Problem
Conventional data communication systems are inadequate in handling high-bandwidth data transfer and error correction, particularly in removing intersymbol interference and burst errors, which affects the accuracy and efficiency of data transmission.
Innovation Solution
The implementation of a feedforward equalization module, decision feedback equalization module, and error detection module with maximum likelihood sequence detection (MLSD) techniques, including a reduced-state trellis path, to process data signals and correct errors, specifically targeting burst errors and intersymbol interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional feedforward equalization and decision feedback equalization are used, then data transmission is enabled, but burst errors and intersymbol interference cannot be effectively removed
Solution Approach 1:
The patent segments the error detection and correction process into distinct modules: feedforward equalization module for initial signal processing, decision feedback equalization module for removing intersymbol interference, and maximum likelihood sequence detection module for detecting and correcting burst errors. This segmentation allows each module to specialize in specific error types while maintaining overall system manageability and effectiveness.
2Measurement precision
If full-fledged MLSD systems are implemented, then high accuracy in error detection is achieved, but processing complexity and power consumption increase
Solution Approach 1:
The patent implements a reduced-state trellis path approach within the maximum likelihood sequence detection module, which performs partial MLSD operations focused specifically on detecting error events rather than complete sequence detection. This partial action achieves sufficient error detection accuracy for correcting burst errors while significantly reducing processing complexity and power consumption compared to full-fledged MLSD systems.
Solution Approach 2:
The patent applies local quality by implementing a reflection cancelation module that specifically targets and removes reflection noises at particular points in the signal path, and uses a reduced-state trellis that focuses computational resources on the most probable error events. This localized approach concentrates processing power where it is most needed rather than uniformly across the entire signal processing chain.
3Strength
If FFE module amplifies signal amplitude, then signal strength increases, but noise amplification also occurs
Solution Approach 1:
The patent employs feedback mechanisms in the decision feedback equalization module that uses previously detected symbols to cancel out intersymbol interference and estimate noise components. This feedback allows the system to distinguish between amplified signal components and amplified noise, enabling more sophisticated noise filtering and correction in subsequent processing stages.
Data Source
AI summary
The present invention is directed to data communication. More specifically, an embodiment of the present invention provides an error correction system. Input data signals are processed by a feedforward equalization module and a decision feedback back equalization module. Decisions generated by the decision feedback equalization module are processed by an error detection module, which determines error events associated with the decisions. The error detection module implements a reduced state trellis path. There are other embodiments as well.