Tensor Product Coding With Iterative Outer Code for Lower Overhead
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Solution Overview
Problem
Linear block codes with shorter input block lengths offer improved performance with Viterbi detectors but incur higher overhead, resulting in a tradeoff between coding gain and code rate, limiting the effectiveness of tensor product code-based systems in data recording and communication.
Innovation Solution
The implementation of tensor product codes incorporating an iterative outer parity hiding code, such as Turbo or low density parity check codes, which iteratively refine codeword estimates using reliability metrics, combined with inner codes and error correction mechanisms to enhance decoding efficiency and reduce overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If linear block codes with shorter input block lengths are used, then coding gain is improved, but code rate penalty increases
Solution Approach 1:
The code is divided into an inner code and an outer code, where the inner code handles short block length error correction and the outer code provides additional redundancy. This segmentation allows the system to achieve the reliability benefits of short block codes while maintaining higher overall code rate through the outer code's structure.
Solution Approach 2:
The patent combines two different coding schemes (inner code and outer code) into a composite coding structure. The inner code uses short block length codes for immediate error correction, while the outer code provides additional protection, creating a composite system that achieves both high reliability and acceptable code rate.
2Reliability
If linear block codes with shorter input block lengths are used, then performance with Viterbi detector is improved, but overhead increases
Solution Approach 1:
The overhead is segmented between the inner code and outer code. The inner code contributes minimal overhead due to its short block length, while the outer code's overhead is optimized through iterative decoding, achieving detection performance improvement with controlled overall overhead.
Solution Approach 2:
The iterative decoder uses feedback loops to exchange reliability information between the inner and outer code decoders. This feedback mechanism allows the system to achieve better detection performance by progressively refining error estimates, thereby improving performance without proportionally increasing overhead.
3Reliability
If iterative codes are used, then error detection and correction is improved, but decoding complexity increases
Solution Approach 1:
The decoding process is segmented into separate inner code decoder and outer code decoder stages. Each decoder handles a specific portion of the error correction task, allowing the use of iterative decoding techniques where needed while keeping other parts simpler, thus managing overall decoding complexity.
Solution Approach 2:
The inner code decoding is performed first as a preliminary step before outer code decoding. This preliminary action removes a significant portion of errors early in the process, reducing the burden on the outer code decoder and thereby lowering the overall decoding complexity while maintaining high error detection and correction capability.
Data Source
AI summary
Systems and methods are provided for encoding a stream of datawords based on a tensor product code to provide a stream of codewords, and detecting and decoding a stream of received data based on a tensor product code to provide a decoded stream of data. In one aspect, the tensor product code is based on two codes including an inner code and an outer parity hiding code, where the outer parity hiding code is an iterative code. In certain embodiments, the outer parity hiding code is a Turbo code or a low density parity check (LDPC) code.