GRAND Decoder Architecture for Code-Agnostic Low-Latency Decoding
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Solution Overview
Problem
Conventional channel decoding methods are limited by their dependency on specific encoding schemes, leading to high computational complexity and latency, especially in achieving high rates and shorter code lengths, and are not adaptable to different coding schemes without re-designing the decoder.
Innovation Solution
The development of a universal decoder using the Guessing Random Additive Noise Decoding (GRAND) algorithm, which generates error vectors in decreasing order of probability to identify errors in the received signal, allowing for decoding without knowledge of the encoding scheme and achieving high efficiency, low latency, and compatibility with various coding schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional channel decoding methods are used, then decoding can be performed for specific encoding schemes, but computational complexity and latency increase, and adaptability to different coding schemes is lost
Solution Approach 1:
The GRAND decoder is designed as a universal decoder that can decode any linear code without requiring code-specific design modifications. It achieves this by generating error vectors in decreasing order of probability and checking them against the received signal, making it adaptable to different coding schemes while maintaining a single unified decoder architecture
2Productivity
If conventional channel decoding methods are used, then decoding for specific codes is achieved, but computational complexity increases
Solution Approach 1:
The GRAND decoder performs preliminary action by generating error vectors in decreasing order of probability before actual decoding. This allows the decoder to check the most likely error patterns first, reducing the average computational complexity while maintaining high decoding speed for moderate redundancy codes
3Reliability
If conventional channel decoding methods are used, then error correction is achieved, but latency increases
Solution Approach 1:
By generating and checking error vectors in decreasing order of probability, the GRAND decoder performs preliminary error pattern identification. This approach reduces decoding latency because the correct error vector is likely to be found early in the sequence, maintaining reliability while reducing the time lost during decoding
Data Source
AI summary
Described is a decoder suitable for use with any communication or storage system. The described decoder has a modular decoder hardware architecture capable of implementing a noise guessing process and due to its dependency only on noise, the decoder design is independent of any encoder, thus making it a universal decoder. Hence, the decoder architecture described herein is agnostic to any coding scheme.