Adaptive Self-Linearization for Real-Time Nonlinearity Compensation
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Solution Overview
Problem
Existing signal processing systems face challenges in compensating for nonlinearities without a training signal, as they require known signal characteristics, making it impractical and costly, especially when system nonlinearities vary due to factors like temperature, aging, or signal dynamics.
Innovation Solution
An adaptive self-linearization module that corrects nonlinear distortion in real-time by separating and processing received signals into reference and target components, using adaptive filters and DSPs to generate a replica distortion signal, allowing for calibration without a known training signal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a training signal with known characteristics is used to correct nonlinearity, then the linearization accuracy is improved, but the manufacturing cost increases and the device complexity increases
Solution Approach 1:
The system performs self-linearization by using the received distorted signal itself as the reference input for the adaptive filter. The adaptive filter automatically adjusts its coefficients to model the nonlinear distortion without requiring external training signals with known characteristics. This self-service approach eliminates the need for costly training signal generation and injection hardware while achieving effective linearization compensation.
Solution Approach 2:
The adaptive filter creates a replica of the distorted signal by modeling the nonlinear channel characteristics. This replica is then subtracted from the received signal to recover the original transmitted signal. The copying mechanism allows the system to reconstruct and remove distortion without needing the original undistorted training signal, thereby reducing manufacturing complexity and cost.
2Measurement precision
If a training signal is used during manufacturing to correct nonlinearity, then the initial linearization performance is improved, but the device cannot adapt to changing system conditions such as temperature variations, aging, and signal dynamics
Solution Approach 1:
The system employs an adaptive filter with time-varying coefficients that continuously adjust to track changes in nonlinear distortion characteristics. The filter coefficients are dynamically updated based on the incoming signal statistics and distortion patterns, enabling the system to adapt to temperature variations, component aging, and changing signal conditions in real-time without requiring re-training or manual intervention.
Solution Approach 2:
The system uses feedback from the received distorted signal to continuously refine the adaptive filter's model of the nonlinear channel. By monitoring the distortion characteristics in real-time and adjusting the filter coefficients accordingly, the system maintains optimal linearization performance under varying operating conditions. The feedback mechanism enables automatic compensation for drift and changes without external control.
3Ease of operation
If traditional linearization methods are used, then the system requires complex training procedures and known signal characteristics, but the adaptive self-linearization method simplifies the process while maintaining effectiveness
Solution Approach 1:
The adaptive self-linearization system automatically performs calibration by using the received signal itself as the reference input. The adaptive filter autonomously adjusts its coefficients to model the nonlinear distortion without requiring external training signals or manual calibration procedures. This self-service approach simplifies operation while maintaining linearization effectiveness through continuous adaptive optimization.
Solution Approach 2:
The system changes the parameters of the adaptive filter (coefficients, tap weights) dynamically to optimize linearization performance. By allowing these parameters to adapt and evolve based on the incoming signal characteristics and distortion patterns, the system achieves effective linearization without requiring fixed pre-calibrated parameters or known training sequences, thereby simplifying operation while maintaining reliability.
Data Source
AI summary
A signal processing method includes receiving an unknown signal that includes a distorted component and an undistorted component, and performing self-linearization based at least in part on the unknown signal to obtain an output signal that is substantially undistorted, wherein performing self-linearization includes adaptively generating a replica distortion signal that is substantially similar to the distorted component, and subtracting the replica distortion signal from the unknown signal to obtain the output signal.