Dynamic LLR Scaling for Forward Error Correction Decoding
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Solution Overview
Problem
Decoders face challenges in maintaining decoding accuracy due to varying log-likelihood ratio (LLR) values caused by changing channel conditions, modulation schemes, and signal power levels, particularly for data bits with low LLR values close to zero.
Innovation Solution
A dynamic scaling scheme is implemented to scale LLR values based on histograms generated from subsets of data bits, expanding the LLR value range of less reliable bits to the maximum, while ensuring higher reliability bits do not exceed the maximum value range, thereby improving decoder performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a fixed scaling factor is used for all LLR values, then the decoding process is simple and fast, but decoding accuracy deteriorates for data bits with low LLR values
Solution Approach 1:
The patent implements dynamic scaling by adjusting the scaling factor based on the actual distribution of LLR values in the received signal. Instead of using a fixed scaling factor, the system dynamically determines the scaling factor by analyzing the LLR histogram and identifying the most significant bin, allowing the scaling process to adapt to varying channel conditions and modulation schemes.
Solution Approach 2:
The patent changes the scaling parameter (scaling factor) based on the observed LLR value distribution. By calculating the scaling factor from the most significant bin of the LLR histogram, the system optimizes the scaling parameter to match the actual signal characteristics, thereby improving decoding accuracy for low LLR values while maintaining reasonable computational complexity.
2Reliability
If LLR values are expanded to maximum range, then decoding accuracy for low LLR values improves, but high LLR values may exceed the maximum value range
Solution Approach 1:
The patent applies local quality by differentiating the scaling needs of different LLR value ranges. By identifying the most significant bin in the LLR histogram and calculating the scaling factor based on that specific region, the system optimizes scaling for the critical low LLR values while preventing overflow for high LLR values through the natural distribution characteristics of the data.
3Reliability
If dynamic scaling based on LLR histogram is implemented, then decoding accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by focusing the scaling optimization only on the most significant bin of the LLR histogram rather than processing all LLR values equally. This selective approach concentrates computational resources on the critical region (low LLR values) that most impacts decoding accuracy, while avoiding unnecessary processing of already-reliable high LLR values.
Data Source
AI summary
Embodiments described herein provide a method for dynamically scaling log-likelihood ratio (LLR) values of received data bits before decoding the received data bits. A plurality of data bits are received corresponding to a data packet. A first set of data bits of a first type are determined from the plurality of data bits based on a modulation scheme corresponding to the received data bits. A first LLR histogram is generated corresponding to the first set of data bits of the first type. A first scaling factor is calculated based on the first LLR histogram such that a first LLR value range corresponding to the first set of data bits is expanded to a maximum LLR value range. All LLR values are scaled by the first scaling factor. The scaled LLR values corresponding to the plurality of data bits are sent to a decoder.