Soft Decision Quantization With Unequal Constellation Ranges
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Solution Overview
Problem
Conventional soft decision methods in communication systems suffer from quantizing distortion due to equal partitioning of coordinates, leading to reduced coding gain and increased bit error rates when dealing with noise-interfered signals.
Innovation Solution
A soft decision method that defines distinct coordinate ranges with unequal limits and intervals for each sub-coordinate, allowing for more precise mapping and decoding of noise-interfered signals, thereby enhancing coding gain and reducing bit error rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If equal intervals are used for quantizing coordinates in soft decision method, then hardware implementation is simplified, but quantizing distortion increases leading to reduced coding gain and increased bit error rate
Solution Approach 1:
The patent applies local quality by using different interval sizes for different coordinate ranges. Specifically, the first coordinate range uses a first interval size while the second coordinate range uses a second interval size that is different from the first. This allows the quantizer to adapt to the local characteristics of different regions in the constellation diagram, reducing quantizing distortion in critical areas while maintaining reasonable complexity.
Solution Approach 2:
The patent changes the parameter of interval size based on the coordinate range. By defining different interval sizes for different coordinate ranges (first interval for first range, second interval for second range), the system dynamically adjusts the quantization precision to match the signal characteristics in different regions, thereby improving overall mapping precision without uniformly increasing complexity.
2Ease of manufacture
If conventional soft decision mapping is used, then implementation is straightforward, but coding gain is reduced and bit error rate increases due to quantizing distortion
Solution Approach 1:
The patent implements local quality by applying different quantization strategies to different coordinate ranges. The first coordinate range is quantized with a first interval while the second coordinate range uses a second interval, allowing the system to prioritize precision in regions where it matters most for error performance while maintaining implementation feasibility.
Solution Approach 2:
The patent segments the coordinate space into at least two distinct ranges (first coordinate range and second coordinate range), each handled by the quantizer with different interval settings. This segmentation allows independent optimization of quantization parameters for different regions, improving overall reliability without requiring complete redesign of the quantization architecture.
Data Source
AI summary
A soft decision method for determining a soft decision coordinate associated with a constellation is provided. The soft decision coordinate includes a first soft decision sub-coordinate and a second soft decision sub-coordinate. The method includes receiving an input signal including a coordinate value; defining a first coordinate range on a coordinate axis in the constellation, the first coordinate range having a first limit and a second limit; obtaining the first soft decision sub-coordinate according to the first coordinate range; defining a second coordinate range on the coordinate axis in the constellation, the second coordinate range having a third limit and a fourth limit; and obtaining the second soft decision sub-coordinate according to the second coordinate range; wherein the first and the third limit do not simultaneously equal to the second and the fourth limit.