Adaptive Slicer Constellation Thresholds for Distorted Signal 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 resource consumption.
Innovation Solution
The implementation of dynamic threshold and constellation adaptation logic that adjusts slicer thresholds and constellations based on statistical distributions of data symbols, using comparators and counters to reduce errors and computational intensity.
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 parameters of the slicer thresholds dynamically based on detected signal non-linearity. Instead of using fixed nominal thresholds, the system adjusts threshold parameters to match the actual signal constellation levels, thereby maintaining high demodulation accuracy without requiring computationally intensive calculations to find the closest constellation level.
Solution Approach 2:
The system implements a feedback mechanism where the receiver detects signal non-linearity characteristics and uses this information to adjust the slicer thresholds. This closed-loop approach continuously adapts the thresholds to match the actual signal conditions, improving demodulation accuracy while avoiding complex real-time calculations.
2Device complexity
If fixed nominal thresholds are used in the slicer, then device complexity is reduced, but demodulation accuracy deteriorates due to signal non-linearity
Solution Approach 1:
The patent transforms the static fixed thresholds into dynamic adaptive thresholds. The slicer thresholds are no longer fixed but are adjusted dynamically based on the detected signal non-linearity characteristics, allowing the system to maintain low device complexity while significantly improving demodulation accuracy under varying signal conditions.
Solution Approach 2:
The system changes the threshold parameters from fixed nominal values to adaptive values that reflect the actual signal constellation levels. This parameter adaptation allows the simple slicer structure to achieve high demodulation accuracy by matching thresholds to the distorted signal characteristics.
3Measurement precision
If adaptive threshold adjustment is implemented to compensate for non-linearity, then demodulation accuracy is improved, but device complexity increases
Solution Approach 1:
The patent uses a feedback-based threshold adaptation mechanism where the receiver detects signal non-linearity and adjusts thresholds accordingly. This feedback approach improves demodulation accuracy while keeping the adaptation mechanism relatively simple, avoiding the need for complex computationally intensive algorithms.
Solution Approach 2:
The system implements self-service threshold adaptation where the receiver autonomously detects signal characteristics and adjusts its own thresholds without external intervention. This self-adapting mechanism improves accuracy while maintaining simplicity by using the signal itself to guide the threshold adjustment process.
Data Source
AI summary
System and method of demodulation by adapting constellation values based on statistic distributions of received data symbols. To determine an adapted constellation, 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. For a set of received symbols, a count ratio of symbols falling in a first range to all the symbols in the set is compared with the expected ratio, where the first range is defined as below a first value. The first value is repeatedly adjusted to adjust the first range until the count ratio equals the expected ratio. The final first value is then designated as the optimal adapted constellation.