Decision Equalization with Adaptive Thresholds for Nonlinear Links
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Solution Overview
Problem
Conventional linear adaptive filters are inadequate in addressing the non-linear features of high-speed optical and electrical links, leading to signal distortion and high bit error rates due to intersymbol interference, limiting the data transmission rate improvement in systems like 100 Gbps and beyond.
Innovation Solution
A signal decision equalization method and apparatus that iteratively updates decision thresholds and equalization expectations based on input signals, using asymmetric distributions and maximum likelihood sequence estimation to optimize equalization and reduce bit error rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional linear adaptive filters are used for signal equalization, then the device complexity is low and ease of manufacture is good, but the equalization performance is insufficient and bit error rate is high due to inability to handle non-linear features
Solution Approach 1:
The equalization process is segmented into multiple decision circuits, each handling specific signal levels independently. Each decision circuit has its own decision threshold and equalization expectation parameters, allowing parallel processing of different signal ranges while maintaining overall system performance.
Solution Approach 2:
The decision thresholds and equalization expectations are dynamically updated through iterative optimization processes. The system adapts these parameters based on incoming signal characteristics, enabling the filter to handle non-linear features while maintaining manageable complexity through structured adaptation.
2Manufacturing precision
If linear adaptive filters are used, then the implementation is simple and cost-effective, but the capability to approximate non-linear functions is limited, causing signal distortion
Solution Approach 1:
Different decision circuits employ asymmetric decision thresholds and equalization expectations tailored to specific signal level ranges. This asymmetric structure allows each circuit to optimize for its particular operating range, improving overall equalization accuracy for non-linear signals while maintaining a systematic implementation approach.
Solution Approach 2:
Each decision circuit is configured with local optimization parameters (decision thresholds and equalization expectations) specific to its signal level range. This local quality approach enables precise equalization for different parts of the signal spectrum without requiring a completely complex global solution.
3Reliability
If nonlinear equalization is implemented to address intersymbol interference, then equalization performance improves, but the device complexity and computational requirements increase significantly
Solution Approach 1:
The non-linear equalization task is divided into multiple decision circuits that process different signal levels independently. This segmentation reduces the computational complexity of each individual circuit while achieving overall non-linear equalization performance through the collective action of all circuits.
Solution Approach 2:
The system uses parameter iteration to optimize decision thresholds and equalization expectations. By changing and optimizing these parameters systematically through iterative processes, the system achieves improved bit error rate performance without requiring fundamentally complex architectural changes.
Data Source
AI summary
A signal decision equalization method and apparatus are provided. The method includes: obtaining an input signal; determining a decision circuit of the input signal; obtaining a first group of decision thresholds and a first group of equalization expectations of the decision circuit; determining a decision value of the input signal based on the first group of equalization expectations, the first group of decision thresholds, and the input signal, and outputting the decision value; updating, based on the decision value and the input signal, a first equalization expectation that is in the first group of equalization expectations and that corresponds to the decision value to a second equalization expectation to obtain a second group of equalization expectations; and updating at least one decision threshold in the first group of decision thresholds based on the second equalization expectation to obtain a second group of decision thresholds.