Noise-Guessing Decoder for Fast Near-Capacity Signal Decoding
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Solution Overview
Problem
Existing channel coding systems face challenges in decoding convolutional codes, which are slower than block codes, and struggle to approach channel capacity due to computational impracticality of large block sizes for near-theoretical performance.
Innovation Solution
A decoder that guesses noise sequences instead of codewords, allowing for deterministic decoding with bounded complexity and separating noise inversion from codeword validation, enabling faster decoding speeds and compatibility with various codebooks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If convolutional codes are used to approach channel capacity, then error correction performance is improved, but decoding speed deteriorates
Solution Approach 1:
Instead of guessing codewords directly (traditional approach), the patent inverts the problem by guessing noise sequences. The decoder guesses what noise was added to the transmitted codeword, subtracts the guessed noise from the received signal, and checks if the result is a valid codeword. This inversion transforms a complex probabilistic search into a more manageable deterministic process with bounded complexity.
Solution Approach 2:
The patent introduces noise sequences as an intermediary element between the received signal and the codeword validation process. By guessing and subtracting noise sequences, the decoder creates an intermediate representation that can be efficiently validated against the codebook, separating the noise inversion task from the codeword membership test.
2Reliability
If block size is increased to approach channel capacity with block codes, then error correction performance is improved, but computational complexity increases
Solution Approach 1:
The patent inverts the traditional block code decoding approach by guessing noise sequences rather than searching for codewords. This inversion allows the use of smaller, computationally manageable block sizes while still achieving near-capacity performance, because the noise guessing process has bounded complexity independent of block size.
Solution Approach 2:
The patent performs preliminary noise guessing and subtraction before codeword validation. By pre-processing the received signal to remove guessed noise effects, the decoder simplifies the subsequent validation step and avoids the need for computationally intensive operations on large blocks of data.
3Speed
If deterministic decoding with bounded complexity is used, then decoding speed is improved, but ability to approach channel capacity deteriorates
Solution Approach 1:
The patent resolves this contradiction by inverting the decoding problem to guess noise sequences deterministically. This approach maintains bounded computational complexity for fast decoding while achieving near-capacity performance through systematic noise guessing strategies that exploit the structure of the codebook and channel characteristics.
Solution Approach 2:
The patent employs dynamic noise guessing strategies that adapt to the specific received signal and codebook structure. By dynamically selecting which noise sequences to guess first and how to validate codewords, the decoder achieves both fast deterministic operation and high reliability approaching channel capacity.
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.