Semi-Analytical Soft Demodulation for Non-Square Constellations
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Solution Overview
Problem
Existing flexible demodulation techniques, such as Max-Log-Map, face challenges in complexity and performance for non-square constellations, leading to high computational costs and potential detection performance losses.
Innovation Solution
The proposed solution combines analytical expressions with Look-Up Table (Lut) correspondence tables to approximate the Max-Log demapping process, specifically designed for constellations with Gray or quasi-Gray labeling, such as PSK and APSK constellations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact Max-Log-Map demapping is used, then detection performance is improved, but computational complexity increases significantly
Solution Approach 1:
The demapping process is segmented into two parts: (1) identification of the closest constellation symbol, and (2) calculation of LLRs using pre-stored distance values from LUTs. This segmentation reduces computational complexity by avoiding exhaustive distance calculations for all constellation symbols while maintaining detection performance.
Solution Approach 2:
Distance values between constellation symbols are pre-calculated and stored in Look-Up Tables (LUTs) before the demapping operation. This preliminary action eliminates the need for real-time distance calculations, significantly reducing computational complexity during actual demapping while preserving exact Max-Log-Map performance.
2Device complexity
If LUT-based approximation is used, then computational complexity is reduced, but memory requirements increase
Solution Approach 1:
The LUTs store only the specific distance values needed for the particular constellation configuration (e.g., only distances relevant to 8-PSK or 16-APSK). This local quality approach optimizes memory usage by storing only necessary data rather than all possible constellation distances, reducing memory requirements while maintaining computational efficiency.
3Adaptability or versatility
If conventional demapping is used for non-square constellations, then adaptability is maintained, but performance loss occurs
Solution Approach 1:
The invention provides a universal demapping framework that works for any constellation type (square QAM, non-square QAM, PSK, APSK) by using constellation-specific LUTs. The same basic algorithm structure adapts to different constellations by loading appropriate pre-calculated distance values, maintaining both adaptability and optimal performance for each constellation type.
Data Source
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AI summary
The invention relates to a flexible decoding method for a received digital signal, comprising, for a received signal (xke) in the complex plane and to be interpreted into a vector of Q bits, a calculation of log-likelihood ratios for each bit of the vector. The digital signal is encoded into symbols in the complex plane according to a Gray-type or quasi-Gray labeling law, and a table associates each symbol in the constellation of other symbols (Δα,1 ... Δα,Q) in the complex plane with the method comprising, after receiving said signal (xke), an identification step (100) of the symbol (α*) in the constellation closest to the signal in the complex plane, followed by an extraction step (101, 102) from memory of at least some of said other symbols (Δα,1 ...Δα,Q) associated by the lookup table with said nearest identified symbol, and finally, by a linear combination (105) of products of the integer and imaginary values of said extracted symbols and the received signal, the supply to the receiver controller of values for the log-likelihood ratios (Le (dk,1) ... Le (dk,Q) ) of the Q bits.