Non-uniform Constellation Optimization via Iterative Parameter Search
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Solution Overview
Problem
Designing non-uniform constellations for signal transmission is challenging due to the high number of parameters required, making exhaustive searches unfeasible, especially for high-order constellations, which limits optimization for capacity and Signal-to-Noise Ratio (SNR) performance.
Innovation Solution
A method is developed to generate non-uniform constellations by iteratively modifying parameter values, determining performance based on predetermined measures, and repeating the process until convergence or reaching a threshold, reducing the complexity and increasing computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive search methods are used to optimize non-uniform constellations, then optimization performance for capacity and SNR is improved, but computational complexity and time requirements increase significantly
Solution Approach 1:
The patent segments the constellation optimization problem by dividing the search space into discrete parameter combinations. Instead of exhaustively searching all possible constellation configurations, the method segments the parameter space (amplitude levels, spacing between points) into manageable discrete values, evaluating only a subset that provides sufficient optimization without requiring complete enumeration of all possibilities.
Solution Approach 2:
The patent applies partial action by evaluating only a representative sample of parameter combinations rather than all possible configurations. The method uses heuristic criteria to select a subset of candidate constellations that are likely to be optimal, avoiding the excessive computational burden of exhaustive search while still achieving meaningful optimization of capacity and SNR performance.
2Measurement precision
If the number of parameters for non-uniform constellations is increased to achieve better performance, then capacity and SNR performance are improved, but device complexity and difficulty of optimization increase
Solution Approach 1:
The patent applies local quality by optimizing specific local characteristics of the constellation diagram rather than treating all parameters uniformly. The method focuses on locally optimizing key parameters such as the spacing between adjacent constellation points and the amplitude distribution in specific regions of the complex plane, allowing for performance improvement without requiring complete optimization of all possible parameters.
Solution Approach 2:
The patent systematically changes key parameters (amplitude levels, spacing between points, distribution of constellation points) to achieve optimal performance. The method evaluates different parameter configurations and selects those that maximize capacity and SNR, transforming the complex optimization problem into a manageable parameter search problem with clear performance metrics.
3Productivity
If high-order constellations are used to increase data rate, then capacity is improved, but the number of parameters and computational complexity increase significantly
Solution Approach 1:
The patent segments the high-order constellation optimization into manageable components by analyzing the constellation structure in terms of discrete parameter combinations. For high-order constellations (256-QAM, 1024-QAM and above), the method breaks down the optimization into evaluating specific amplitude levels and spacing parameters rather than treating the entire constellation as a single complex entity, making the computational task feasible.
Solution Approach 2:
The patent applies partial action to high-order constellations by evaluating only the most critical parameter combinations that will have the greatest impact on performance. Instead of exhaustively optimizing all parameters for high-order constellations, the method identifies and optimizes the key parameters (such as minimum distance between points and overall amplitude distribution) that provide the most significant capacity improvement with the least computational effort.
Data Source
AI summary
A method for generating a non-uniform constellation is provided. The method comprises the step of performing a first process, the first process comprising the steps of: obtaining a first constellation defined by one or more parameter values; and generating a second constellation based on the first constellation using a second process. The second process comprises the steps of: obtaining a set of candidate constellations, wherein the set of candidate constellations comprises the first constellation and one or more modified constellations, wherein each modified constellation is obtained by modifying the parameter values defining the first constellation; determining the performance of each candidate constellation according to a predetermined performance measure; selecting the candidate constellation having the best performance as the second constellation.