Pre-Compensation Circuit for Power Amplifier Nonlinearity
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Solution Overview
Problem
Power amplifiers exhibit nonlinearity, leading to degraded bit-error rate and low efficiency, and existing digital pre-distortion methods require complex computational burdens due to large numbers of coefficients, making them impractical for wide signal bandwidth applications.
Innovation Solution
A pre-compensation method and circuit using Bayesian Optimization, Causal Bayesian Optimization, or Dynamic Causal Bayesian Optimization to update parameters and hyperparameters for a digital pre-distortion actuator, reducing computational complexity and improving accuracy by compensating for power amplifier nonlinearity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Volterra series and its derivatives are used for DPD implementation, then the linearization accuracy is improved, but the computational complexity increases due to the large number of coefficients
Solution Approach 1:
The patent extracts only the essential parameters and hyperparameters needed for effective pre-distortion, eliminating the large number of coefficients required by traditional Volterra series methods. This selective extraction maintains linearization accuracy while significantly reducing computational burden.
Solution Approach 2:
The patent employs a simplified pre-distortion model that uses fewer computational resources (cheaper in terms of computation), accepting that the model may need frequent updates or retraining (short-living in terms of model validity) to maintain accuracy, rather than relying on complex permanent structures with many coefficients.
2Reliability
If more advanced DPD algorithms are used to handle memory effects in wide bandwidth signals, then the linearization performance is improved, but the computational burden increases
Solution Approach 1:
The patent performs pre-distortion in advance before the signal reaches the power amplifier, compensating for memory effects and non-linearities proactively. By using Bayesian optimization to efficiently select parameters beforehand, the system achieves good linearization performance without requiring computationally intensive real-time processing during signal transmission.
Solution Approach 2:
The patent changes the approach from using many fixed coefficients to using a smaller set of parameters and hyperparameters that are optimized through Bayesian methods. This parameter transformation allows the system to handle memory effects in wide bandwidth signals with reduced computational burden while maintaining reliability.
3Measurement precision
If a larger number of coefficients are used in DPD algorithms, then the accuracy in modeling PA behavior is improved, but the data requirements increase for statistical confidence
Solution Approach 1:
The patent transforms the traditional approach by changing from many coefficients to fewer parameters and hyperparameters. This parameter reduction means that less input/output data is needed to achieve statistical confidence, as there are fewer variables to estimate. Bayesian optimization further enhances this by efficiently utilizing available data to optimize the reduced parameter set.
Data Source
AI summary
A pre-compensation method for a pre-compensation circuit coupled to a power amplifier to compensate for nonlinearity of the power amplifier includes performing pre-distortion according to at least one parameter or at least one hyperparameter to convert a first input signal received by the pre-compensation circuit into a first pre-distortion output signal, updating the at least one parameter or the at least one hyperparameter according to Bayesian Optimization, Causal Bayesian Optimization, or Dynamic Causal Bayesian Optimization, and performing pre-distortion according to the at least one parameter updated or the at least one hyperparameter updated to convert a second input signal received by the pre-compensation circuit into a second pre-distortion output signal.