Adaptive Constellation Slicer Thresholds for Nonlinear Demodulation
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Solution Overview
Problem
Existing demodulation mechanisms in communication systems face challenges with non-linearity and amplitude compression, leading to errors due to noise and phase interference, as they do not effectively adapt to signal distortions, resulting in increased computational complexity and resource consumption.
Innovation Solution
The solution involves dynamically adapting constellation selection thresholds and levels using statistical distributions to offset nonlinearity, employing threshold and constellation adaptation logic that adjusts thresholds and constellations based on expected ratios and error margins, reducing computational intensity and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computationally intensive calculations are used to identify the actual closest signal constellation level, then demodulation accuracy is improved, but computation resource consumption increases significantly
Solution Approach 1:
The patent changes the parameter of threshold values from fixed nominal thresholds to dynamically adapted thresholds that compensate for non-linearity. By adjusting these threshold parameters based on received signal characteristics, the system achieves accurate constellation level identification without requiring computationally intensive calculations to determine the closest signal level.
Solution Approach 2:
The system performs preliminary adaptation of thresholds before demodulation decisions are made. The threshold adaptation logic pre-calculates adjusted thresholds that account for non-linearity effects, so that when demodulation occurs, the slicer can directly use these pre-adapted thresholds without requiring complex real-time computations.
2Device complexity
If fixed nominal thresholds are used in the slicer, then device complexity is reduced, but demodulation accuracy deteriorates due to non-linearity
Solution Approach 1:
The patent transforms the static fixed thresholds into dynamic adapted thresholds that can adjust to signal conditions. The threshold adaptation logic continuously updates the threshold values based on the statistical distribution of received symbols, allowing the slicer to maintain high accuracy while keeping the overall device complexity manageable through a systematic adaptation mechanism.
Solution Approach 2:
The system implements feedback through the threshold adaptation logic that monitors the distribution of received symbols and adjusts thresholds accordingly. This feedback mechanism ensures that the thresholds remain optimized for current signal conditions, compensating for non-linearity effects without requiring complex real-time recalculations during demodulation.
3Measurement precision
If threshold adaptation logic is implemented to compensate for non-linearity, then demodulation accuracy is improved, but device complexity increases
Solution Approach 1:
The threshold adaptation logic serves itself by using the statistical distribution of received symbols to automatically adjust its own threshold parameters. The system uses the incoming signal data to adapt its thresholds without requiring external calibration or complex control mechanisms, making the adaptation process self-contained and relatively simple to implement.
Solution Approach 2:
The patent replaces complex mechanical or computational systems with a statistical approach. Instead of using computationally intensive algorithms to determine the closest constellation level, the system substitutes a statistical analysis of symbol distributions combined with simple threshold adjustments, achieving comparable or superior accuracy with reduced complexity.
Data Source
AI summary
System and method of adapting thresholds for constellation selection based on statistic distributions of received data symbols. To determine an adapted threshold, an expected ratio of received symbols with values in a certain range is preset based on an expected statistic distribution of data symbols across the multiple constellations. A first and a second ratios are defined based on the expected ratio, the first ratio being the expected ratio minus an error ratio and the second ratio being the expected ratio plus the error ratio. A first value is determined which makes the received symbols in a firs range to constitute the first ratio of a set of slicer inputs. A second value is determined which makes the received symbols in the second range to constitute the second ratio of a set of slicer outputs. The adapted threshold is then obtained based on the first and the second value.