Log Likelihood Ratio Estimation Using Regression Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The computation of log-likelihood ratios (LLRs) in communication systems is complex and computationally intensive, especially for non-Gray coded modulations, where existing methods face challenges in achieving accurate decoding performance due to the non-linear nature of received symbol spaces.
Innovation Solution
The method involves partitioning the received symbol space into regions and using pre-computed multiple linear regression models, including regressors for both real and imaginary parts of received symbols, to estimate LLRs efficiently, with coefficients determined using ordinary least squares (OLS) for each region, allowing for efficient computation of LLRs for both Gray and non-Gray coded modulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct computation methods are used for LLR calculation, then accuracy is maintained, but computational complexity becomes excessively high
Solution Approach 1:
The received symbol space is partitioned into multiple regions, and separate linear regression models are established for each region. This segmentation allows the complex non-linear LLR computation to be broken down into simpler regional linear approximations, reducing overall computational complexity while maintaining accuracy within each region.
Solution Approach 2:
The patent transforms the LLR computation from direct non-linear calculation to a two-stage process: first determining which region the received symbol falls into, then applying the corresponding pre-computed linear regression model. This parameter change (from direct computation to region-based lookup and linear approximation) significantly reduces computational burden.
2Device complexity
If region-based linear regression models are used, then computational complexity is reduced, but accuracy may deteriorate
Solution Approach 1:
Different linear regression models are established for different regions of the received symbol space, with each model optimized for its specific region's characteristics. This local quality approach ensures that the LLR computation is accurate for symbols within each region, while the overall system maintains good performance across the entire symbol space.
Solution Approach 2:
The linear regression models are pre-computed and stored in lookup tables before actual LLR computation is needed. This preliminary action allows the system to replace complex real-time non-linear calculations with simple region identification and pre-computed model application, maintaining both speed and accuracy.
3Productivity
If pre-computed regression models are stored in lookup tables, then computation speed is improved, but memory requirements increase
Solution Approach 1:
The lookup tables are organized by regions, with each region having its own pre-computed regression coefficients. This segmentation allows the memory to be divided into manageable sections, and only the relevant regional model needs to be loaded and applied based on the received symbol's location, optimizing both speed and memory usage.
Data Source
AI summary
A method includes communicating data in a channel. Received symbols for the data correspond to points of a received symbol space respectively. First and second dimensions of the received symbol space correspond to a real part and an imaginary part of the received symbols respectively. A first received symbol for the data is obtained. A first region of the received symbol space for the first received symbol is determined. A first regression model associated with the first region and a first bit of the first received symbol is retrieved from a storage. The first regression model includes a plurality of regressors. A first log-likelihood ratio (LLR) for the first bit of the first received symbol is estimated using the first regression model.