Continuous-Time Equalizer Adaptation Without Channel Training
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Solution Overview
Problem
Existing decision-feedback equalization techniques face challenges in adapting analog equalizer parameters without prior channel training or measurement, particularly in adapting AEQ transfer functions based on coefficients related to the impulse response.
Innovation Solution
A method is introduced that adjusts the transfer function of a linear equalizer using a gradient signal determined by comparing sampled values of an input signal with error signals, allowing for self-adaptation of AEQ parameters, specifically the high-frequency peaking factor, without requiring a priori channel training or measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prior channel training or measurement is used to adapt AEQ parameters, then the adaptation accuracy is improved, but the system complexity and preprocessing requirements increase
Solution Approach 1:
The system uses the equalizer's own output and error signals to adapt its parameters through the LMS algorithm, eliminating the need for external channel training sequences or measurement equipment. The equalizer adapts autonomously using readily available signals within the system.
Solution Approach 2:
The adaptation mechanism uses feedback from the error signal (difference between equalized output and desired signal) to continuously adjust the AEQ parameters. This closed-loop feedback enables real-time adaptation without requiring prior channel knowledge or training phases.
2Measurement precision
If prior channel training is required for AEQ parameter adaptation, then the initial adaptation precision is improved, but the system's adaptability to dynamic channel changes deteriorates
Solution Approach 1:
The equalizer parameters are made dynamic and continuously adjustable through the gradient-based adaptation algorithm. The system transitions from static pre-calibrated parameters to dynamic real-time adaptation, allowing the equalizer to track and respond to time-varying channel characteristics.
Solution Approach 2:
The system performs self-adaptation by continuously monitoring its own performance through the error signal and automatically adjusting parameters without external intervention or re-training, enabling it to adapt to dynamic channel conditions autonomously.
3Speed
If analog circuit techniques are employed to adapt AEQ parameters, then the adaptation speed is improved, but the manufacturing complexity and precision requirements increase
Solution Approach 1:
The patent replaces complex analog adaptation circuits with a digital signal processing implementation of the LMS algorithm. The gradient computation and parameter updates are performed digitally, simplifying manufacturing while maintaining fast adaptation speeds through efficient digital computation.
4Reliability
If pre-calibration and channel measurement are required, then the initial equalization performance is improved, but the loss of time for calibration increases
Solution Approach 1:
The system performs preliminary adaptation actions continuously in the background using the LMS algorithm, so that when full equalization is needed, the parameters are already optimized. This eliminates the need for separate calibration phases while maintaining high initial performance.
Solution Approach 2:
The adaptation process operates continuously rather than in discrete calibration phases. The gradient-based parameter updates occur ongoing using continuously available error signals, eliminating idle calibration time while maintaining equalization performance throughout operation.
Data Source
AI summary
Described embodiments provide a method of adjusting configurable parameters of at least one linear equalizer in a communication system. A transmitting device applies an input signal to a receiver. The at least one linear equalizer equalizes the input signal. A sampler generates one or more sampled values of the input signal. A data detector digitizes the sampled values of the input signal. At least one error detection module generates an error signal based on one or more of a plurality of sampled values of the input signal and a target value. An adaptation module determines a gradient signal based on a comparison of one or more of the plurality of sampled values of the input signal and one or more of the plurality of values of the error signal. The adaptation module adjusts a transfer function of the at least one linear equalizer based on the determined gradient signal.