Noise-Guessing Decoder With Symbol Masking for Noisy Channels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current decoding algorithms face challenges in efficiently decoding data over noisy channels, particularly when the code rate exceeds the channel capacity, and they often require complex joint designs of codebooks and decoders, which can lead to high computational complexity and inefficiencies in handling noise variability.
Innovation Solution
A method for decoding coded symbols by iteratively guessing noise sequences and removing their effects from received data, using soft information to generate symbol masks that identify likely errors, allowing for approximate maximum likelihood decoding with bounded complexity and the ability to approach channel capacity without requiring extensive code-dependent decoding mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If maximum likelihood decoding is used to achieve optimal decoding performance, then decoding accuracy is improved, but computational complexity increases significantly
Solution Approach 1:
The decoding process is segmented into two independent stages: (1) channel decoding to recover the codeword from the received signal, and (2) noise parameter estimation to determine the noise sequence. This segmentation allows each stage to be optimized independently, reducing overall computational complexity while maintaining maximum likelihood performance.
Solution Approach 2:
The patent inverts the traditional decoding approach by first estimating the noise parameters from the received signal and then using this noise information to simplify the codeword decoding process. This inversion transforms the difficult joint optimization problem into two simpler sequential problems, significantly reducing computational complexity.
2Measurement precision
If code-dependent decoding algorithms are used to achieve good performance for specific codes, then decoding accuracy is improved, but adaptability to different codebooks deteriorates
Solution Approach 1:
The decoding algorithm is designed with universal applicability to work with any linear block code, convolutional code, or rateless code without requiring code-specific modifications. The noise estimation component and syndrome-based decoding component can handle diverse code structures uniformly, achieving both good performance and high adaptability.
Solution Approach 2:
The algorithm adapts to different codebooks by dynamically adjusting parameters such as the syndrome computation method, noise estimation order, and decoding threshold based on the specific code characteristics. This parameter adaptation allows the same core algorithm to achieve optimal performance across different code types and rates.
3Reliability
If iterative noise guessing is performed to handle noise variability, then robustness to noise changes is improved, but processing time increases
Solution Approach 1:
The algorithm performs preliminary noise estimation using low-complexity methods (such as minimum mean square error estimation or decision-directed estimation) before the main decoding process. This preliminary action provides an initial noise estimate that guides the subsequent iterative refinement, reducing the number of iterations needed and thus processing time.
Solution Approach 2:
The decoding process incorporates feedback mechanisms where the decoded codeword is used to refine the noise estimate, which in turn improves the next decoding attempt. This feedback loop converges quickly to the optimal solution, achieving high noise robustness with limited iterations and acceptable processing time.
Data Source
Figure 1
Figure 2
Figure 2A
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. In various embodiments, soft information is used to generate a symbol mask that identifies the collection of symbols that are suspected to differ from the channel input, and only these are subject to guessing. This decoder embodies or approximates maximum likelihood (optionally with soft information) decoding for any 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.