SOVA Coefficient Adaptation Using Reliable Outputs and Sub-Sampling
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Solution Overview
Problem
Existing techniques for adapting coefficients in soft output Viterbi algorithm (SOVA) detectors for low-density parity-check (LDPC) codes face instability at low signal-to-noise ratios and incur high hardware costs due to long and variable decoding latency in LDPC decoders.
Innovation Solution
Implementing a process that selectively stores and adapts coefficients based on reliability values and data patterns, using a sub-sampling scheme to reduce hardware requirements and buffer sizes, and employing a data-dependent noise prediction module to improve coefficient adaptation in SOVA detectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If coefficient adaptation uses LDPC decoder outputs, then decoding accuracy is improved, but hardware cost increases due to long and variable decoding latency
Solution Approach 1:
The patent segments the coefficient adaptation process into two distinct paths: one using Viterbi detector outputs for initial adaptation, and another using LDPC decoder outputs for final refinement. This segmentation allows the system to benefit from LDPC's high accuracy without requiring the entire system to wait for LDPC decoding, thereby reducing the hardware buffer requirements and complexity associated with long decoding latency.
Solution Approach 2:
The patent performs preliminary coefficient adaptation using Viterbi detector outputs before the LDPC decoding process completes. This preliminary action provides initial coefficient values that can be used immediately, reducing the waiting time and hardware resources needed while the longer LDPC decoding process runs in the background for final accuracy improvement.
2Measurement precision
If all detector outputs are stored for coefficient adaptation, then adaptation accuracy is improved, but buffer size and hardware requirements increase
Solution Approach 1:
The patent applies local quality by selectively storing only those detector outputs that meet specific reliability criteria (e.g., minimum signal-to-noise ratio thresholds, confidence level requirements). Instead of uniformly storing all outputs, the system identifies and stores only the high-quality samples that will most effectively improve coefficient adaptation, thereby reducing buffer size while maintaining or improving adaptation accuracy.
Solution Approach 2:
The patent changes the parameter of selection criteria for stored outputs, using dynamic thresholds based on signal conditions, error rates, and reliability metrics. By adjusting these parameters adaptively, the system optimizes which outputs are stored, ensuring high adaptation accuracy with minimal buffer requirements.
Data Source
AI summary
A system for adapting coefficients of a soft output Viterbi algorithm (SOVA) is disclosed. The system includes a receiver configured to select an output of an SOVA detector at least in part based on a criterion. The receiver is configured to store the selected output of the SOVA detector. The receiver is further configured to store a signal that corresponds to the stored selected output of the SOVA detector, wherein the input to the SOVA detector is derived from the signal. The receiver is further configured to adapt a plurality of coefficients of the SOVA detector at least in part based on the stored selected output of the SOVA detector, the stored signal, and a corresponding data pattern. The system includes an interface coupled to the receiver and configured to receive samples.