Adaptive Pre-Distortion for Nonlinear Power Amplifier Distortion
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Solution Overview
Problem
Existing communication systems face significant challenges in mitigating power amplifier non-linear distortion, particularly in data-over-co-channel-signal systems, where conventional compensation methods are not applicable and pre-distortion mapping is difficult to obtain due to high computational complexity and inaccessible regressor vectors.
Innovation Solution
The implementation of three non-linear compensation methods using mean squared error (MSE) of demodulated symbol outputs to generate error signals for pre-distortion mapping, including the Fittest Survivor Algorithm (FSA), least-mean-square (LMS) type single parameter estimation, and a combination of FSA and LMS for adaptive pre-distortion, which simplify the compensation mechanism and reduce computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional compensation methods are used, then PA distortion mitigation is achieved, but the methods are not applicable to data-over-co-channel-signal systems and have high computational complexity
Solution Approach 1:
The patent changes the fundamental parameter being optimized from intermediate pulse-shaped signals to final demodulated data symbols. This parameter transformation enables the compensation method to be applicable to data-over-co-channel-signal systems where conventional methods fail, as it directly addresses the actual output quality metric that matters for system performance.
Solution Approach 2:
The patent extracts and eliminates the requirement for inaccessible regressor vectors and complex pre-distortion mapping calculations by using a direct symbol error minimization approach. This extraction removes the computational complexity barrier while maintaining distortion mitigation effectiveness.
2Reliability
If pre-distortion mapping is obtained through conventional methods, then distortion compensation is achieved, but computational complexity is prohibitively high and regressor vectors are inaccessible
Solution Approach 1:
The patent replaces the complex mechanical/computational system of calculating pre-distortion mapping from intermediate signals with a direct statistical optimization approach using demodulated symbol errors. This substitution eliminates the need for inaccessible regressor vectors and complex iterative calculations, making the system computationally feasible while maintaining or improving compensation accuracy.
Solution Approach 2:
The system uses the actual demodulated output symbols themselves to generate the error signals needed for optimization, rather than requiring external reference signals or complex intermediate calculations. This self-service approach simplifies the computational burden and makes the method self-contained and implementable in practical systems.
3Productivity
If sophisticated signal processing techniques are used, then high data rates are supported, but sensitivity to distortions increases
Solution Approach 1:
The patent implements a feedback mechanism where demodulated data symbols are fed back to generate error signals that drive the optimization of the compensation filter. This closed-loop feedback directly addresses the distortion impact on the actual data output, enabling sophisticated signal processing to maintain high data rates while actively compensating for increased distortion sensitivity through continuous adaptation.
Data Source
AI summary
Compensation methods and apparatus are disclosed for mitigating non-linear distortion of high power amplifiers used in communication transmitters, based on the minimization of the symbol error, in particular, for communication systems transmitting data embedded in existing co-channel signals. To overcome the difficulty of constructing pre-distortion mapping from the symbol error, an adaptive algorithm is disclosed to update compensation parameters for pre-distortion mapping. Another method exploits a test signal interval in the co-channel signal. During a known constant co-channel signal, distortion is estimated as a complex number and used to construct compensation parameters. The latter is further expanded by employing the adaptive algorithm for the non-test signal intervals.