Sparse Channel Equalization via Symbol Sub-block Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing equalization methods for sparse transmission channels are plagued by high implementation complexity and unrealistic channel models, particularly in aeronautical communication links, due to their reliance on interdependent trellises and zero-power secondary paths.
Innovation Solution
A method that demultiplexes received symbols into sub-blocks, applies independent equalization using a MAP algorithm on a trellis structure, subtracts interference from secondary paths, and iteratively refines the equalization process to achieve parallelization and reduced complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If parallel lattice detection algorithms or MAP algorithms are used for equalization in sparse channels, then demodulation and decoding performance is improved, but implementation complexity increases significantly
Solution Approach 1:
The received block of N symbols is segmented into L sub-blocks through demultiplexing, where each sub-block contains N/L symbols. This segmentation allows the equalization to be performed independently on each sub-block using simpler algorithms, avoiding the need for complex parallel lattice or MAP algorithms while maintaining good performance through the iterative interference cancellation process.
2Measurement precision
If interference suppression with interdependent parallel trellises is implemented, then equalization accuracy is improved, but parallelization capability is lost and device complexity increases
Solution Approach 1:
The interference from multipath components is extracted and canceled iteratively. In each iteration, the equalizer processes sub-blocks independently without requiring interdependent trellises, and the interference from other paths is calculated and subtracted from the received signal. This extraction approach achieves good equalization accuracy while maintaining independence and parallelization capability across sub-blocks.
3Device complexity
If zero-power secondary path model is used, then device complexity is reduced, but measurement precision of channel model deteriorates
Solution Approach 1:
The channel model dynamically adapts through iterative processing. Rather than using a static zero-power approximation, the system iteratively estimates and cancels interference from secondary paths, allowing the effective channel model to become more accurate with each iteration while keeping the initial complexity low. This dynamic approach achieves both low initial complexity and high final accuracy.
Data Source
Figure 1~2
Figure 3
Figure 4
AI summary
Method for equalizing a signal comprising a plurality of modulated symbols, said method comprising the following steps applied to a block of N received symbols: - A first equalization step of said block comprising: i. Demultiplexing (110) the N received symbols by a factor L so as to generate a predetermined number L of symbol subblocks, each containing a downsampled version by a factor L of said block of N received symbols, ii. Independent equalization (121, 122, ... 12L) of each subblock using an identical equalization algorithm, iii. Multiplexing (130) the equalized symbols of each subblock to obtain a block of N equalized symbols, - A path-related interference suppression step other than the two highest-power paths comprising: i.The generation (140) of an interference term resulting from the influence, on said equalized symbols, of all paths of the channel of the impulse response of the transmission channel except the two paths of highest power, ii. The subtraction (150) of said interference term from the symbols of the block of N received symbols, - A second equalization step equal to a second iteration of the first equalization step.