Polar Decoding with Adaptive List Size for Error-Rate Variation
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Solution Overview
Problem
The complexity of polar decoding procedures increases with larger list sizes, which affects the error-correcting capability and efficiency in decoding polar codes, particularly in channels with varying error rates.
Innovation Solution
A decoding system comprising a first decoder that generates intermediate decoding data using a variable list size in a first decoding algorithm and a second decoder that further decodes this data in a second algorithm, allowing for dynamic adjustment of list size based on error rates to optimize decoding complexity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the list size is increased to achieve better error-correcting capability, then the error-correcting performance is improved, but the decoding complexity increases
Solution Approach 1:
The patent applies dynamics by making the list size variable rather than fixed. The list size is dynamically adjusted based on channel conditions and error rates, allowing the decoder to adapt to different scenarios. When error rates are low, a smaller list size reduces complexity, while when error rates are high, a larger list size improves error-correcting capability.
Solution Approach 2:
The patent changes the parameter of list size from a static value to a dynamic parameter that can be adjusted based on channel conditions. By monitoring error rates and channel quality, the system modifies the list size parameter to optimize the trade-off between error-correcting performance and decoding complexity.
2Reliability
If the list size is increased to improve error-correcting performance, then the reliability is improved, but the decoding time increases
Solution Approach 1:
The list size is dynamically adjusted based on channel conditions and error rates, allowing the decoder to use smaller list sizes when error rates are low (reducing decoding time) and larger list sizes when error rates are high (improving error-correcting performance).
Solution Approach 2:
The system changes the list size parameter dynamically based on monitored error rates and channel quality, optimizing the balance between error-correcting performance and decoding time by adapting to actual channel conditions rather than using a fixed conservative value.
3Reliability
If a fixed large list size is used to ensure reliability, then the error-correcting capability is maintained, but the decoding complexity increases for all channel conditions
Solution Approach 1:
Instead of using a fixed large list size, the patent implements a dynamic list size that adapts to channel conditions. The list size is adjusted based on error rates and channel quality metrics, allowing the system to maintain reliability when needed while reducing complexity when channel conditions are good.
Solution Approach 2:
The system transitions from a static parameter (fixed list size) to a dynamic parameter (variable list size) that changes based on channel conditions and error rates, optimizing the trade-off between reliability and complexity for different operating scenarios.
Data Source
AI summary
The present disclosure provides a decoding system and method. The decoding system comprises a first decoder and a second decoder. The first decoder is configured to generate an intermediate decoding data by decoding a code data. The second decoder, coupled to the first decoder, wherein the second decoder is configured to generate a plain data by decoding the intermediate decoding data.