Noise-Guessing Block Code Decoding for Fast Capacity-Approaching Recovery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing channel coding systems face inefficiencies in decoding speed and accuracy, particularly with convolutional codes, which require complex probabilistic models, and block codes struggle to approach channel capacity due to large block sizes and computational impracticality.
Innovation Solution
The proposed system decodes block codes by guessing noise sequences rather than codewords, allowing for deterministic decoding with bounded complexity and separating noise inversion from codeword validation, enabling faster decoding speeds and approaching channel capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If convolutional codes are used to approach channel capacity, then decoding accuracy improves, but decoding speed deteriorates due to complex probabilistic models and algorithms
Solution Approach 1:
The patent inverts the traditional decoding approach by guessing the noise sequence instead of guessing the codeword directly. This inversion transforms the complex probabilistic decoding problem into a simpler deterministic noise guessing problem, achieving both high decoding accuracy and fast decoding speed
Solution Approach 2:
The patent segments the decoding process into two independent parts: noise guessing and codeword validation. The noise guessing step operates deterministically with bounded complexity, while the codeword validation step checks membership in the codebook. This segmentation separates the computationally intensive probabilistic modeling from the actual decoding task
2Measurement precision
If block codes use large block sizes to approach channel capacity, then decoding accuracy improves, but computational complexity increases making it impractical
Solution Approach 1:
The patent inverts the traditional block code decoding approach by guessing the noise sequence rather than searching for the codeword in the codebook. This inversion reduces the computational complexity from exponential in block size to linear, making large block sizes computationally practical while maintaining high decoding accuracy
Solution Approach 2:
The patent changes the parameter being guessed from codeword (which requires searching through the entire codebook) to noise sequence (which has bounded complexity). This parameter change transforms the decoding problem from computationally intractable to efficiently solvable, enabling block codes to approach channel capacity with practical computational resources
3Speed
If traditional block code decoding guesses codewords directly, then decoding speed is maintained, but decoding accuracy deteriorates due to inability to approach channel capacity
Solution Approach 1:
The patent inverts the guessing target from codeword to noise sequence. By guessing the noise sequence and subtracting it from the received signal, the patent achieves both fast decoding (comparable to traditional block codes) and high accuracy (approaching channel capacity), resolving the trade-off between speed and accuracy
Data Source
AI summary
Devices and methods described herein decode a sequence of coded symbols by guessing noise. In various embodiments, noise sequences are ordered, either during system initialization or on a periodic basis. Then, determining a codeword includes iteratively guessing a new noise sequence, removing its effect from received data symbols (e.g. by subtracting or using some other method of operational inversion), and checking whether the resulting data are a codeword using a codebook membership function. This process is deterministic, has bounded complexity, asymptotically achieves channel capacity as in convolutional codes, but has the decoding speed of a block code. In some embodiments, the decoder tests a bounded number of noise sequences, abandoning the search and declaring an erasure after these sequences are exhausted. Abandonment decoding nevertheless approximates maximum likelihood decoding within a tolerable bound and achieves channel capacity when the abandonment threshold is chosen appropriately.