Symbol Demodulation Using Convolution-Based Reliability Processing
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Solution Overview
Problem
Current demodulation and demapping architectures and algorithms in communication systems lead to inefficient resource and power consumption, posing a bottleneck due to high computational complexity, especially in scenarios with large numbers of signal states and channel imperfections.
Innovation Solution
A demodulation apparatus and method utilizing fast convolution operations, including kernel convolutions and discrete Fourier transforms, to determine reliability information for symbol constellations, reducing computational complexity and improving adaptability to channel noise and imperfections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional demodulation and demapping algorithms are used, then reliability information can be obtained, but computational complexity increases and resource consumption increases
Solution Approach 1:
The patent segments the symbol constellation into multiple partitions or regions, and processes each partition separately using convolution operations. This divides the complex global demodulation problem into smaller, more manageable local problems, reducing overall computational complexity while maintaining reliability information accuracy.
Solution Approach 2:
The patent replaces conventional iterative demodulation algorithms with convolution-based processing in the frequency domain. By transforming the problem from time-domain iterative processing to frequency-domain convolution, computational efficiency is dramatically improved while preserving the ability to extract reliable symbol information.
2Reliability
If conventional demodulation algorithms are used, then symbol detection can be performed, but power consumption increases
Solution Approach 1:
The patent substitutes power-intensive iterative processing with efficient convolution operations in the frequency domain. The convolution-based approach requires fewer computational iterations and can be implemented using fast Fourier transform algorithms, significantly reducing power consumption while maintaining symbol detection accuracy.
Solution Approach 2:
The patent transforms the processing domain from time-domain to frequency-domain, changing the fundamental parameter space in which demodulation occurs. This parameter transformation enables the use of convolution theorem and fast Fourier transform techniques, which are computationally more efficient and consume less power than conventional time-domain iterative algorithms.
3Productivity
If high-order modulation schemes are used, then data rate increases, but computational complexity increases
Solution Approach 1:
The patent partitions the high-order symbol constellation into multiple smaller regions or clusters, and applies convolution processing to each partition separately. This segmentation approach prevents the computational complexity from scaling exponentially with modulation order, enabling efficient processing of high-order schemes like 64-QAM, 256-QAM, and beyond.
Solution Approach 2:
The patent moves the processing from the symbol domain to the frequency domain using Fourier transformation. This dimensional change introduces a new processing dimension where convolution operations can be performed efficiently, decoupling computational complexity from the direct scaling of modulation order and enabling high-data-rate transmission with manageable complexity.
4Measurement precision
If detailed reliability information is computed for all symbol states, then accuracy improves, but processing time increases
Solution Approach 1:
The patent divides the computation of reliability information into parallel convolution operations for different symbol partitions. By processing multiple partitions simultaneously through parallel convolution, the patent achieves comprehensive reliability information for all symbol states without sequential processing delays, thus maintaining accuracy while reducing processing time.
Solution Approach 2:
The patent replaces time-consuming iterative reliability computations with direct convolution operations in the frequency domain. The convolution theorem allows simultaneous computation of reliability metrics for all symbol states through a single transformation and convolution operation, dramatically reducing processing time while preserving measurement precision.
Data Source
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.