Tree-Structured FEC Coding for Lower Bit Error Ratios
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Solution Overview
Problem
Communication networks face signal degradation due to polarization mode dispersion, polarization dependent loss, state of polarization rotation, amplified spontaneous emission, and chromatic dispersion, leading to increased bit error ratios and reduced signal quality.
Innovation Solution
A transmitter device employs forward error correction (FEC) encoding using a tree structure to generate encoded vectors by calculating multiple FEC codewords, including parity information, and transmitting these over communication channels, utilizing mathematical operations like Galois Field addition and exclusive OR operations, and applying FEC codes such as Bose, Chaudhuri, and Hocquenghem codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If forward error correction encoding is applied to correct signal degradation, then bit error ratio is reduced, but device complexity increases
Solution Approach 1:
The encoding process is divided into multiple independent stages: generating three or more separate codewords from the data vector, where each codeword is calculated through recursion of mathematical operations. This segmentation allows the complex encoding task to be broken down into manageable components that can be processed independently and combined to form the final encoded vector.
Solution Approach 2:
The patent transforms the traditional single-codeword encoding approach into a multi-dimensional encoding space by generating three or more codewords. The encoded vector is then calculated from these multiple codewords, effectively adding a dimensional aspect to the encoding process that improves error correction capability while maintaining manageable complexity through structured computation.
2Reliability
If multiple FEC codewords are calculated from data vector, then error correction capability is improved, but calculation complexity increases
Solution Approach 1:
The patent performs preliminary calculations by generating multiple codewords from the data vector before final encoding. Each codeword is pre-calculated through recursive mathematical operations, allowing the system to prepare multiple error correction paths in advance. This preliminary action enables more robust error correction while organizing the computational complexity into structured, manageable steps.
Solution Approach 2:
The patent replaces traditional mechanical or hardware-based error correction mechanisms with mathematical field operations, specifically Galois Field addition and exclusive OR operations. This substitution allows for efficient calculation of multiple codewords through algebraic operations rather than complex physical or mechanical processes, reducing implementation complexity while maintaining strong error correction capability.
3Productivity
If systematic FEC codes are used, then encoding efficiency is improved, but parity information requirements increase
Solution Approach 1:
The patent merges multiple codewords into a single encoded vector through systematic combination. By calculating the encoded vector from three or more codewords using structured mathematical operations, the system consolidates the parity information requirements into an integrated encoding process. This merging approach maintains encoding efficiency while organizing parity information generation in a coordinated manner.
Data Source
AI summary
A transmitter generates an encoded vector by encoding a data vector, the encoded vector representing payload information and parity information. The encoding is mathematically equivalent to calculating three or more forward error correction (FEC) codewords from the data vector and then calculating the encoded vector from the codewords, at least one codeword being calculated from at least one recursion of a mathematical operation, and at least one codeword comprising more than 6 terms. The transmitter transmits a signal representing the encoded vector over a communication channel. A receiver determines a vector estimate from the signal and recovers the data vector from the vector estimate by sequentially decoding the codewords, wherein at least one codeword that is decoded earlier in the decoding enhances an estimate of at least one codeword that is decoded later in the decoding.