Syndrome-Guided LDPC Decoder Control for Trapping Set Convergence

The patent performs preliminary actions by gathering syndrome statistics data before the actual decoding operation. The controller collects syndrome data from previous iterations and analyzes trapping set patterns in advance, then uses this pre-analyzed information to guide the decoding process. This preliminary analysis enables the system to anticipate and avoid trapping sets, improving decoding accuracy without adding complex real-time processing during decoding operations.

The patent implements a feedback mechanism where the controller continuously monitors syndrome data from decoding iterations and uses this feedback to adjust decoding parameters. When trapping sets are detected through syndrome pattern recognition, the controller modifies the decoding process in subsequent iterations. This closed-loop feedback system dynamically adapts the decoding strategy based on observed performance, resolving the contradiction between maintaining low complexity and achieving high reliability.

The controller performs preliminary analysis of syndrome statistics to identify trapping set patterns before the decoder enters prolonged iteration cycles. By pre-processing syndrome data and recognizing trapping set signatures in advance, the system can detect when it is approaching a trapping set and take corrective action earlier, avoiding the need for numerous additional iterations that would otherwise be required to escape the trapping set.

The patent introduces dynamic parameter adjustment based on syndrome statistics. The controller dynamically modifies decoding parameters such as normalization factors or message passing thresholds when trapping sets are detected through syndrome pattern recognition. This dynamic adaptation allows the decoder to respond adaptively to trapping set conditions, breaking out of convergence stalls without requiring a fixed increase in iteration count, thus reducing the time loss while maintaining decoding accuracy.