Decision Feedback Equalizer With SER-Biased Adaptation for ISI
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Solution Overview
Problem
Existing communications methods and systems are overly power hungry and spectrally inefficient, failing to effectively address inter-symbol interference and non-linearity in communication channels.
Innovation Solution
A decision feedback equalizer utilizing a symbol error rate biased adaptation function is implemented, which includes a system configured for low-complexity, highly-spectrally efficient communications. This system uses a mapper, pulse shaping filter, timing pilot insertion, transmitter and receiver front-ends, and equalization and sequence estimation circuits to optimize symbol transmission and reception, specifically designed to handle non-linearity and inter-symbol interference through partial response pulse shaping and feedback equalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional communications methods are used, then system simplicity is maintained, but spectral efficiency is poor and power consumption is high
Solution Approach 1:
The patent implements decision feedback equalization where the equalizer uses feedback from previously detected symbols to compensate for inter-symbol interference. The adaptation function continuously adjusts equalizer coefficients based on detected symbol errors, creating a closed-loop system that improves spectral efficiency through iterative optimization while managing complexity through structured feedback mechanisms
Solution Approach 2:
The equalizer employs dynamic adaptation of its coefficients through the symbol error rate biased adaptation function. The system transitions from static conventional equalization to dynamic adaptive equalization, where parameters are continuously optimized based on real-time channel conditions and detected errors, enabling improved spectral efficiency through time-varying optimization
2Reliability
If conventional equalization is used, then computational complexity is low, but inter-symbol interference and non-linearity are not effectively addressed
Solution Approach 1:
The decision feedback equalizer structure separates equalization into feed-forward and feedback paths. The feedback path specifically targets inter-symbol interference by using previously detected symbols to cancel interference in current symbol detection. The symbol error rate biased adaptation function provides feedback for coefficient optimization, improving reliability through iterative error correction
Solution Approach 2:
The adaptation function applies different weighting and processing to different aspects of the equalization process. By biasing the adaptation toward symbol error rate minimization rather than simple mean-square error, the system optimizes local performance metrics that directly impact bit-error rate, achieving improved reliability through targeted local optimization rather than uniform processing
3Productivity
If spectral efficiency is increased through advanced modulation, then data rate improves, but power consumption increases
Solution Approach 1:
The equalizer performs self-adaptation through the symbol error rate biased adaptation function, automatically optimizing its coefficients based on detected errors without requiring external intervention or complex training sequences. This self-service capability allows the system to maintain high spectral efficiency while minimizing power consumption by avoiding unnecessary reconfiguration and external optimization overhead
Data Source
AI summary
One or more embodiments describe a decision feedback equalizer utilizing symbol error rate biased adaptation function for highly spectrally efficient communications. A method may be performed in a decision feedback equalizer (DFE). The method may include determining values of tap coefficients used by the DFE based. The tap coefficients may be determined based on an error signal that is based on an estimated inter-symbol-correlated (ISC) signal. The tap coefficients may be determined based on a set of error vector(s), where each error vector in the set represents a difference between estimated symbols generated in the receiver and expected symbols. Determining the values of the tap coefficients may include using a symbol error rate function that estimates the actual symbol error rate in the receiver, wherein the symbol error rate function receives as input the set of error vector(s).