GRAND Noise Decoding With Query-Limited Error Pattern Search
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Solution Overview
Problem
Guessing Random Additive Noise Decoding (GRAND) techniques require a large number of codebook membership queries, leading to increased decoding latency and hardware requirements due to the need for extensive bit flipping and evaluation.
Innovation Solution
Implementing the GRANDAB technique, which limits the number of codebook membership queries by generating candidate error patterns using circular shift registers and evaluating them for codebook membership, with the option to abandon the guessing procedure if a preset query threshold is exceeded, thereby reducing the Hamming weight of considered error patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GRAND technique is used to decode based on guessing noise, then decoding accuracy is improved, but the number of codebook membership queries increases significantly
Solution Approach 1:
The patent pre-generates error patterns of different Hamming weights in descending order before decoding. This preliminary arrangement allows the decoder to systematically test error patterns from most to least likely, reducing the average number of queries needed while maintaining high decoding accuracy through the structured search approach.
Solution Approach 2:
The patent dynamically adjusts the decoding process by implementing an abandonment mechanism that stops the guessing procedure when a preset threshold of codebook membership queries is exceeded. This dynamic adaptation allows the system to balance between decoding accuracy and query efficiency, preventing excessive queries while maintaining reliability within acceptable bounds.
2Reliability
If extensive bit flipping and evaluation are performed in GRAND, then decoding accuracy is improved, but decoding latency increases
Solution Approach 1:
Error patterns are pre-generated and organized by Hamming weight before the actual decoding process. This preliminary preparation eliminates the need for real-time generation and sorting of error patterns during decoding, significantly reducing decoding latency while maintaining the systematic evaluation approach that ensures high decoding accuracy.
Solution Approach 2:
The abandonment mechanism dynamically terminates the decoding process when the query threshold is reached, preventing excessive latency. This dynamic stopping criterion ensures that decoding latency is bounded while still allowing sufficient time for accurate decoding when the number of required queries remains below the threshold.
3Reliability
If extensive bit flipping and evaluation are performed in GRAND, then decoding accuracy is improved, but hardware requirements increase
Solution Approach 1:
Error patterns are pre-generated and stored in a structured format before decoding operations. This preliminary organization allows the hardware to efficiently retrieve and evaluate error patterns without complex real-time generation logic, reducing hardware complexity while maintaining the comprehensive error pattern evaluation needed for high decoding accuracy.
Solution Approach 2:
The abandonment mechanism provides a simple hardware-friendly control structure that limits the maximum number of operations. This dynamic threshold-based approach prevents the hardware from needing to support arbitrarily complex decoding operations, thereby reducing hardware requirements while still achieving high decoding accuracy within the operational limits.
4Loss of time
If the number of codebook membership queries is limited, then decoding latency is reduced, but decoding accuracy may deteriorate
Solution Approach 1:
Error patterns are pre-generated and sorted by Hamming weight in descending order before decoding. This preliminary arrangement ensures that the most likely error patterns are evaluated first, maximizing decoding accuracy within the limited query budget. The systematic ordering allows the decoder to achieve high accuracy with fewer queries by focusing on the most probable error cases.
Solution Approach 2:
The abandonment mechanism dynamically adapts to the specific decoding scenario by stopping when the query threshold is reached. This dynamic behavior allows the system to achieve acceptable decoding accuracy for typical cases within the latency constraints, while the pre-generated error patterns ensure that the most critical error cases are evaluated first.
Data Source
AI summary
There is provided a method comprising, at a data receiver, receiving a channel codeword from a data sender over a noisy data channel, generating a plurality of candidate error patterns, the plurality of candidate error patterns comprising a plurality of one-bit error patterns and a plurality of multiple-bit error patterns generated from the plurality of one-bit error patterns, evaluating the plurality of candidate error patterns for codebook membership, based on the channel codeword, and outputting an estimated codeword when a codebook membership constraint is satisfied for a given candidate error pattern.