Associative LDPC Decoder Architecture for Lower Complexity Decoding
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Solution Overview
Problem
Current communication systems face challenges in efficiently decoding low density parity check (LDPC) codes due to high error rates and complexity in iterative decoding processes, particularly in noisy communication channels.
Innovation Solution
The implementation of a low density parity check (LDPC) decoder using processing elements (PEs) based on associative processing, which perform iterative decoding through check node and variable node update units, and employ multi-threshold decision schemes to generate soft and hard decision values, reducing error rates and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative decoding processes are used for LDPC codes, then decoding accuracy is improved, but computational complexity increases
Solution Approach 1:
The decoder is divided into multiple processing elements (PEs), each responsible for specific rows of the parity check matrix. Each PE is further segmented into check node update units and variable node update units, distributing the iterative decoding workload across multiple independent units that can operate in parallel, thereby reducing overall computational complexity while maintaining decoding accuracy.
Solution Approach 2:
The decoder implements dynamic threshold adjustment during the iterative decoding process. Threshold values are adapted based on the current iteration state and decoding progress, allowing the system to optimize its performance at different stages of decoding. This dynamic approach improves decoding accuracy without requiring uniformly high computational resources throughout the entire process.
2Productivity
If multiple processing elements are used for parallel decoding, then decoding speed is improved, but hardware resources increase
Solution Approach 1:
Each processing element is designed as a universal unit capable of handling multiple rows of the parity check matrix. The check node update units and variable node update units within each PE can process different data depending on the current iteration and row being decoded. This multi-functionality allows fewer PEs to handle the entire decoding process, reducing hardware resources while maintaining parallel processing speed.
Solution Approach 2:
The processing elements operate continuously through multiple iterations without requiring reconfiguration or idle periods. Each PE maintains its computational state and continues processing different portions of the decoding task across iterations, ensuring that hardware resources are fully utilized throughout the decoding process, thereby achieving high decoding speed with efficient resource usage.
3Reliability
If adaptive threshold adjustment is implemented, then decoding performance is improved, but control complexity increases
Solution Approach 1:
The decoder implements feedback mechanisms where the outcome of each iteration is used to adjust threshold values for subsequent iterations. The control logic monitors decoding progress and error patterns, then adaptively modifies thresholds to optimize performance. This feedback-based approach systematically improves decoding performance while keeping control complexity manageable through structured adaptation rules.
Data Source
AI summary
An embodiment of a decoder comprises processing elements operating on associative processing. The processing elements may comprise a logic and memory element. Each row of the decoder comprises one or more associative processing elements controlled by a row control element to determine the two minimum values. Each column comprises one or more associative processing elements, an input processing element, and a column control element to determine hard decision bits. The usage of processing elements to construct a decoder may reduce the gate count and decrease the interconnects used to couple the elements.