Fast Symbol Processing with Convolution-Based Reliability Demapping
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Solution Overview
Problem
Current demodulation and demapping processes in communication systems are inefficient, leading to high resource and power consumption and bottlenecks, especially in scenarios with high-order constellations, multiple access channels, and MIMO channels, where the receiver faces a large number of signal states and imperfections.
Innovation Solution
A demodulation apparatus and method utilizing fast convolutions and discrete grids to determine reliability information for symbol constellations, incorporating kernel convolutions and discrete masks to efficiently handle additive noise and imperfections, enabling efficient demodulation and demapping with reduced computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional demodulation and demapping processes are used, then reliability information can be obtained, but computational complexity and resource consumption increase significantly
Solution Approach 1:
The patent segments the symbol constellation into multiple subsets or partitions, allowing reliability information to be computed separately for each subset. This division reduces the computational burden by breaking down the complex overall calculation into smaller, more manageable segments that can be processed independently and then combined.
Solution Approach 2:
The patent performs preliminary discretization of the signal space into a grid structure before demodulation and demapping operations. By pre-establishing the grid and identifying relevant constellation points in advance, the system reduces the computational complexity of subsequent reliability calculations, as the search space is already constrained and organized.
2Productivity
If high-order constellations are used to increase data rate, then productivity improves, but the number of signal states increases leading to higher computational complexity
Solution Approach 1:
The patent divides high-order constellations into multiple lower-complexity subsets or partitions. Each subset contains fewer signal states that can be processed more efficiently. This segmentation allows the system to maintain high data rates through high-order modulation while reducing the computational complexity by handling smaller subsets independently rather than processing all states simultaneously.
Solution Approach 2:
The patent focuses computational resources on determining reliability information for specific subsets or partitions of the constellation rather than uniformly processing all signal states. This partial action approach concentrates effort on the most relevant or critical portions of the signal space, achieving adequate reliability information with reduced overall computational complexity.
3Measurement precision
If comprehensive reliability information is computed for all constellation points, then measurement precision improves, but loss of time increases due to extensive calculations
Solution Approach 1:
The patent computes reliability information by segmenting the constellation into multiple subsets and processing each subset separately. This segmentation enables the system to achieve comprehensive coverage of all constellation points while reducing computation time, as parallel or sequential processing of smaller subsets is more efficient than monolithic processing of the entire constellation.
Solution Approach 2:
The patent performs preliminary identification and discretization of relevant constellation points before computing reliability information. By pre-processing the signal space and organizing constellation points into structured subsets in advance, the system reduces the time required for actual reliability calculations while maintaining measurement precision across all points.
Data Source
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AI summary
Improvements to a demodulation process or to a demapping process are described. The improvements include that a signal comprising at least one modulated symbol from a labelled symbol constellation is obtained, and reliability information for at least one piece in the labelled symbol constellation is determined, by performing at least one convolution between a kernel and states or a subset of states associated with the labelled symbol constellation, wherein a piece is a symbol label, or a part of a symbol label or a subset comprising parts.