Symbol Decision Confidence Generation for High-Speed Receivers
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Solution Overview
Problem
High-speed communication systems face challenges in generating both low-cost and reliable hard-decision decoding with a corresponding soft-decision output for each symbol or bit, as traditional soft-decision MLSE algorithms like the Soft Output Viterbi Algorithm are complex and impractical for high-speed architectures.
Innovation Solution
Implementing a Forney algorithm-based decision generation component that generates hard-decision estimations and calculates a confidence level for each symbol or bit, using a Forney-based soft MLSE algorithm in a digital signal receiver, allowing parallel processing of symbol blocks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional soft-decision MLSE algorithms like the Soft Output Viterbi Algorithm are used, then soft-decision output with confidence level is achieved, but device complexity increases making it impractical for high-speed architectures
Solution Approach 1:
The patent replaces the complex Soft Output Viterbi Algorithm with a simpler Forney algorithm-based approach that generates soft-decision outputs. This simpler algorithm acts as a 'cheap' alternative that achieves the same functional goal (producing confidence levels for each symbol) without the computational burden, making it suitable for high-speed architectures where complexity must be minimized.
Solution Approach 2:
The patent changes the algorithmic parameters and computational approach from the traditional Viterbi algorithm to the Forney algorithm. This parameter change in the algorithm selection allows the system to generate soft-decision outputs with confidence levels while reducing computational complexity, enabling implementation in high-speed communication systems.
2Reliability
If high-order partial response equalization is used to overcome high insertion loss, then Bit Error Rate performance improves, but bandwidth requirement increases
Solution Approach 1:
The patent employs high-order partial response equalization (PR1 or PR2) which changes the system parameters to achieve better BER performance. By selecting appropriate partial response orders and combining them with the Forney algorithm-based soft-decision output, the system optimizes the trade-off between BER performance and bandwidth utilization in high-insertion-loss channels.
3Reliability
If hard-decision FEC is used to reduce burst error effects, then error correction is achieved, but soft-decision information is lost
Solution Approach 1:
The patent generates soft-decision outputs with confidence levels using the Forney algorithm before the hard-decision FEC processing occurs. This preliminary generation of soft information allows the system to preserve confidence level data that can be used by the FEC decoder to improve error correction performance, rather than losing this information by going directly to hard decisions.
Solution Approach 2:
The system uses the soft-decision confidence level information as feedback to enhance the hard-decision FEC process. The confidence levels provide additional information that can guide the FEC decoding process, improving overall error correction capability while maintaining the benefits of hard-decision FEC for burst error reduction.
Data Source
AI summary
A receiver including a first component to receive a signal including a sequence of symbols and generate an equalized signal with an estimated sequence of symbols corresponding to the signal. The receiver further includes a second component to generate, based on the equalized signal, a decision including a sequence of one or more bits that represent each symbol of the estimated sequence of symbols. The second component of the receiver further generates a confidence level corresponding to the decision, wherein the confidence level is based on a comparison of a first probability that the equalized signal comprises two or more errors and a second probability that the equalized signal comprises zero errors.