Serial Receiver Equalizer Calibration Without Known Data Patterns
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Solution Overview
Problem
Existing methods for equalizing high-speed serial data over non-ideal communication channels require prior knowledge of the channel or use of repetitive traffic patterns, which is not feasible in environments where channel characteristics are unknown.
Innovation Solution
A method that uses error detection through decoding of 10-bit symbols to calibrate equalizers in a serial receiver, allowing for dynamic adjustment of equalization settings without prior knowledge of the incoming signal or channel characteristics, by sweeping equalization parameters and measuring bit error rates to determine optimal settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If channel measurement and modeling is used to determine equalization settings, then equalization accuracy is improved, but the method becomes inapplicable when channel characteristics are unknown
Solution Approach 1:
The system performs self-calibration by using its own error detection capabilities to measure channel effects and automatically adjust equalization settings without external measurement equipment or prior channel knowledge. The receiver uses decoding errors from incoming data to infer channel characteristics and optimize equalization parameters autonomously.
Solution Approach 2:
The system implements a feedback loop where decoding errors are detected, used to measure channel effects, and then feed back to adjust equalization settings. This closed-loop approach allows continuous optimization of equalization based on actual reception performance rather than relying on pre-measured channel characteristics.
2Measurement precision
If repetitive traffic patterns are used for equalization calibration, then measurement accuracy is improved, but the method loses applicability to unknown or non-repetitive traffic
Solution Approach 1:
The system uses the incoming traffic itself as the measurement signal by detecting decoding errors that occur during normal operation. Rather than requiring external test patterns, the system leverages the actual data stream to infer channel characteristics and optimize equalization settings for the specific traffic being received.
Solution Approach 2:
The equalization calibration method works universally with any traffic pattern that can be decoded, including both repetitive and non-repetitive patterns. The error detection and measurement mechanism is general-purpose, accommodating various encoding schemes and traffic types without requiring pattern-specific calibration procedures.
3Measurement precision
If eye measurement circuitry is used to determine equalization settings, then equalization optimization is improved, but device complexity increases
Solution Approach 1:
The system extracts channel measurement information from the decoding error signals already present in the data path, rather than adding separate measurement circuitry. By taking out the useful measurement data from the error detection process, the system avoids the complexity of dedicated eye diagram measurement equipment while still achieving equalization optimization.
Solution Approach 2:
The error detection and measurement circuitry serves multiple functions: it detects decoding errors for data integrity monitoring and simultaneously measures channel effects for equalization calibration. This multi-functional approach eliminates the need for separate measurement equipment, reducing overall device complexity while maintaining measurement accuracy.
Data Source
AI summary
A method and apparatus for calibrating an equalizer. The method comprises the steps of defining one or more equalization parameters and calculating a bit error rate of a signal for one or more values of a first of the one or more equalization parameters by counting at least one of running disparity errors and incorrect symbol errors. A value for the first of the one or more equalization parameters which provides the lowest bit error rate is set.