Decision Feedback Equalizer Error Detection With Reduced-State MLSD
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Solution Overview
Problem
Conventional data communication systems are inadequate in handling high-bandwidth data transfer demands and error correction, particularly in removing intersymbol interference and burst errors, which affects the accuracy and efficiency of data transmission.
Innovation Solution
An error correction system that incorporates feedforward equalization, decision feedback equalization, and maximum likelihood sequence detection using a reduced-state trellis path to process data signals, effectively removing intersymbol interference and burst errors, and implementing a reflection cancellation module to enhance error detection and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional feedforward equalization and decision feedback equalization are used, then intersymbol interference can be reduced, but burst errors and reflection noises remain unaddressed
Solution Approach 1:
The system segments the error correction function into multiple specialized modules: FFE for basic equalization, DFE for intersymbol interference removal, reflection cancelation module for reflection noise, and maximum likelihood sequence detection for burst error correction. Each module handles a specific type of distortion independently, achieving comprehensive error correction that conventional single-stage equalizers cannot provide.
Solution Approach 2:
The reflection cancelation module acts as an intermediary between the DFE and the maximum likelihood sequence detection module. It specifically targets and removes reflection noises before the signal proceeds to burst error correction, creating a staged approach where each module prepares the signal for the next stage of processing.
2Measurement precision
If maximum likelihood sequence detection is implemented with full-state trellis, then error detection accuracy is maximized, but computational complexity increases significantly
Solution Approach 1:
The invention extracts and removes the most problematic error types (intersymbol interference via DFE, reflection noises via reflection cancelation) before applying maximum likelihood sequence detection. This preprocessing extraction allows the MLSD to focus only on burst error detection, enabling the use of reduced-state trellis paths that maintain accuracy for the remaining error type while significantly reducing computational complexity.
Solution Approach 2:
Instead of applying full-state trellis maximum likelihood sequence detection to correct all error types, the system applies partial action by using reduced-state trellis paths that are optimized specifically for burst error detection. The other error types are handled by simpler, dedicated modules, achieving overall high accuracy without the excessive computational burden of full-state MLSD.
3Strength
If feedforward equalization amplifies signal amplitude, then signal strength is improved, but noise amplification and intersymbol interference increase
Solution Approach 1:
The decision feedback equalization module uses feedback from detected symbols to generate correction signals that are subtracted from the equalized output. This feedback mechanism specifically targets and removes intersymbol interference introduced by the FFE amplification, allowing the system to maintain signal strength while correcting the harmful effects of amplitude amplification.
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.