Adaptive LLR Scaling for Low-Complexity LDPC Decoding
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Solution Overview
Problem
Existing LDPC decoding algorithms in 5G communication systems face challenges with high computational complexity and quantization errors, particularly in complex fading channels and varying modulation modes, leading to reduced decoding performance.
Innovation Solution
A data processing method that classifies LLR elements based on modulation mode and signal-to-noise ratio, extracts feature information, calculates a scale factor, and scales the LLR elements to reduce quantization errors and improve decoding accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If soft-decision decoding algorithm is used, then decoding performance is improved, but computational complexity increases significantly
Solution Approach 1:
The patent applies parameter changes by transforming the soft-decision decoding algorithm into a fixed-point transformation. This changes the numerical representation parameters from floating-point to fixed-point, reducing computational complexity while maintaining acceptable decoding performance through quantization of LLR elements
2Device complexity
If fixed-point transformation is applied to reduce complexity, then computational complexity is reduced, but quantization errors are introduced
Solution Approach 1:
The patent applies preliminary action by performing classification and scaling operations on LLR elements before the main decoding process. The scale factor calculation and LLR scaling are done in advance to optimize the distribution of quantized values, reducing quantization errors that would otherwise occur during the decoding process
Solution Approach 2:
The patent applies dynamics by making the scale factor adaptive rather than static. The scale factor is dynamically calculated based on the classification of LLR elements and their feature information, allowing the system to adapt to different channel conditions and modulation modes, thereby reducing quantization errors across varying operating conditions
3Device complexity
If quantization is performed on LLR elements, then computational complexity is reduced, but decoding accuracy deteriorates in complex fading channels
Solution Approach 1:
The patent applies local quality by treating different LLR elements differently through classification. Instead of uniform quantization, the patent categorizes LLR elements based on their characteristics and applies specific scaling operations to each category. This localized approach preserves important local features in the LLR distribution, maintaining decoding accuracy in complex fading channels while still enabling quantization
4Ease of manufacture
If uniform quantization is applied, then implementation is simplified, but adaptability to different modulation modes and channel conditions is reduced
Solution Approach 1:
The patent applies dynamics by implementing an adaptive scale factor calculation mechanism that responds to different modulation modes and channel conditions. The classification module dynamically categorizes LLR elements based on current operating conditions, and the scale factor is adjusted accordingly, enabling the system to adapt to various modulation modes (QPSK, 16QAM, 64QAM, 256QAM) and channel conditions without requiring completely different implementation schemes
Data Source
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AI summary
The present application provides a data processing method, a data processing apparatus, a decoder, a network device and a computer-readable storage medium. The data processing method comprises: classifying log likelihood ratio (LLR) elements according to a modulation mode, a preset quantization threshold of a decoder and a signal-to-noise ratio, so as to obtain a classification result; extracting feature information of each category in the classification result; calculating a scaling factor according to the feature information of each category; and performing scaling processing on the LLR elements according to the scaling factor.