Power Amplifier Linearization with Robust Coefficient Updating
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Solution Overview
Problem
Existing digital compensation techniques for non-linear circuits, such as power amplifiers in wireless transmitters, face challenges with poor convergence and robustness due to sensitivity to device variations and operating conditions, leading to ineffective linearization.
Innovation Solution
A method is introduced that computes robust coefficient values for digital compensators by combining current and prior sets of quantities characterizing nonlinearity, using weighted averages and basis functions to update coefficients, ensuring stability and convergence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If digital pre-distortion is applied to compensate for non-linearities in power amplifiers, then signal quality and linearity are improved, but the system becomes sensitive to device variations and operating conditions, leading to poor robustness
Solution Approach 1:
The patent implements dynamic adaptation of the digital pre-distorter coefficients based on operating conditions. The system continuously monitors device variations and operating parameters, then adjusts the compensation coefficients in real-time to maintain optimal performance across different conditions, thereby improving robustness while preserving signal linearity.
Solution Approach 2:
The patent changes the parameters of the digital pre-distorter based on observed device variations and operating conditions. By adapting the compensation coefficients to match current device state, the system maintains effective linearization despite process variations, temperature changes, and supply voltage fluctuations.
2Manufacturing precision
If high-order polynomial terms are used in the digital pre-distorter to improve linearization accuracy, then signal fidelity is improved, but computational complexity increases exponentially
Solution Approach 1:
The patent segments the high-order polynomial computation into manageable components using efficient algorithms. By breaking down the complex calculations into smaller, modular operations and using basis function expansions, the system achieves high linearization accuracy while keeping computational complexity tractable through structured computation.
Solution Approach 2:
The patent uses a selective approach to polynomial terms, incorporating only the necessary order and number of terms required to achieve adequate linearization performance. This partial action approach avoids the exponential complexity of full high-order expansions while maintaining sufficient signal fidelity for practical applications.
3Manufacturing precision
If coefficient values are continually updated to maintain linearization performance, then signal quality is maintained, but convergence is poor and computational resources are consumed
Solution Approach 1:
The patent implements feedback mechanisms that monitor the effectiveness of coefficient updates and adjust the adaptation process accordingly. By using feedback from performance metrics and error signals, the system achieves reliable convergence while maintaining linearization performance, avoiding unnecessary computations and resource consumption.
Solution Approach 2:
The patent employs periodic updates of coefficient values rather than continuous updates, using scheduled adaptation intervals triggered by significant operating condition changes. This periodic approach maintains linearization performance while reducing computational overhead and improving convergence behavior compared to continuous updating.
Data Source
AI summary
A linearization system computes a robust set of quantities characterizing a nonlinearity of a transmit chain including a power amplifier by combining a current set of quantities characterizing the nonlinearity of the transmit chain with a weighted combination of prior sets of quantities characterizing the nonlinearity of the transmit chain.