BICM-ID Receiver Simplification Using Quadrant Symbol APP Processing
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Solution Overview
Problem
Current bit-interleaved coded modulation with iterative decoding (BICM-ID) systems have high implementation complexity, which prevents their adoption in state-of-the-art receivers despite their potential for improving power efficiency and spectral efficiency.
Innovation Solution
A receiver design that simplifies signal processing by generating symbol a posteriori probabilities based on Euclidean distances computed only for the quadrant to which received symbols belong, and using extrinsic-information-based symbol probability log-likelihood ratios without converting extrinsic information into log-likelihoods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Euclidean distance based symbol probability is generated at every BICM-ID outer iteration instead of calculating once and storing in memory, then the system achieves better iterative decoding performance, but the logic area increases significantly occupying approximately 95% of the decoder area
Solution Approach 1:
The patent segments the Euclidean distance calculation by dividing the constellation into quadrants. Only symbols within the relevant quadrant are considered for distance calculation, reducing the number of calculations from all M constellation points to approximately M/4 points. This segmentation maintains decoding performance while reducing logic area requirements.
Solution Approach 2:
The patent applies partial action by computing Euclidean distances only for a subset of constellation symbols (those in the relevant quadrant) rather than all symbols. This partial computation approach achieves sufficient accuracy for iterative decoding while dramatically reducing the logic area occupation from 95% to a manageable level.
2Productivity
If conventional BICM-ID systems compute symbol probabilities at every outer iteration, then spectral efficiency is improved for non-gray mapped constellations, but the implementation complexity becomes prohibitively high for practical satellite receivers
Solution Approach 1:
The patent divides the constellation space into quadrants and segments the probability computation to only consider symbols within the relevant quadrant. This reduces the computational burden from evaluating all M constellation points to evaluating approximately M/4 points, making practical implementation feasible while preserving spectral efficiency gains.
Solution Approach 2:
The patent applies local quality by focusing computational resources on the local region (relevant quadrant) rather than uniformly processing all constellation points. This localized approach maintains the quality of probability estimation where it matters most while reducing overall complexity to practical levels.
3Reliability
If perfect Gray bit-to-symbol mapping is used for QPSK, then the system achieves modulation constrained capacity with no SNR loss, but this cannot be attained for APSK constellations resulting in SNR penalty
Solution Approach 1:
The patent implements feedback through iterative decoding where soft information is exchanged between the decoder and symbol probability calculator. This feedback mechanism allows the system to overcome the limitations of imperfect Gray mapping by iteratively refining bit estimates, effectively compensating for the SNR penalty that would otherwise result from non-Gray-mappable APSK constellations.
Data Source
AI summary
Systems and methods for processing bit-interleaved coded modulation (BICM) signals from a BICM transmitter to generate information bit estimates of information in the BICM signals, including a decoder to generate the information bit estimates of the information in the received BICM signals and a symbol a posteriori probability (APP) generator to generate first symbol a posteriori probabilities (APPs) by processing the BICM signals based on Euclidean distances derived from the BICM signals, and further based on symbol probability log-likelihood ratios (SPLLRs) provided to the symbol APP generator by an extrinsic-information-based symbol probability log-likelihood ratio (SPLLR) generator. The SPLLR generator generates the SPLLRs directly from extrinsic information based on updated symbol APPs output from the decoder, without converting the extrinsic information into log-likelihoods (LLs), and the decoder generates the information bit estimates based on the first symbol APPs output from the symbol APP generator.