Adaptive Volterra Compensation for RF Nonlinear Distortion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional linearization techniques for power amplifiers and analog-to-digital converters, such as pre-distortion and digital post-processing, are limited in their ability to correct nonlinear distortion, especially in advanced RF systems with high instantaneous bandwidths, and require accurate modeling of amplifier or converter characteristics, which can be inadequate under varying operating conditions.
Innovation Solution
An adaptive Volterra filter algorithm that periodically updates coefficients to reduce nonlinear distortion, using an inverse Volterra filter of order N and an adaptive filter estimator, implemented in an integrated circuit, to dynamically correct for nonlinearities in power amplifiers and analog-to-digital converters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pre-distortion techniques with second-order or third-order polynomial transfer functions are used, then the linearity of power amplifiers is improved, but the accuracy is insufficient for advanced RF systems with very high instantaneous bandwidths
Solution Approach 1:
The patent changes the mathematical parameters of the transfer function from traditional second-order or third-order polynomials to higher-order Volterra series expansions. This parameter change enables accurate modeling of irregular nonlinearities in high-bandwidth RF systems while maintaining computational feasibility through selective truncation of the Volterra series at appropriate orders.
Solution Approach 2:
The patent implements adaptive estimation of Volterra kernel coefficients that can dynamically adjust to varying operating conditions such as temperature, time, and frequency. This dynamic adaptation allows the linearization technique to maintain accuracy across different environmental conditions and signal characteristics, making it versatile for advanced RF systems.
2Device complexity
If a single transfer function is used for pre-distortion, then the implementation is simple, but it is not suitable when operating conditions vary such as temperature, time, or frequency
Solution Approach 1:
The patent segments the single transfer function into multiple Volterra kernels of different orders, each capturing specific nonlinear characteristics. The adaptive estimator then selectively updates only the necessary kernels based on current operating conditions, maintaining simplicity while improving adaptability. This segmentation allows the system to focus computational resources on the most relevant nonlinearities.
Solution Approach 2:
The Volterra series framework provides a universal modeling approach that can represent various types of nonlinearities (memoryless, with memory, irregular patterns) through a single unified mathematical structure. The adaptive estimation mechanism makes this universal model applicable across different operating conditions by dynamically adjusting kernel coefficients, eliminating the need for multiple separate transfer functions.
3Measurement precision
If higher-order Volterra filters are used to improve linearity accuracy, then the computational complexity increases
Solution Approach 1:
The patent applies partial action by selectively truncating the Volterra series expansion at optimal orders based on the specific application requirements and available computational resources. Instead of implementing all possible higher-order kernels, the system implements only those orders necessary to achieve the desired linearity accuracy, reducing computational complexity while maintaining sufficient performance.
Solution Approach 2:
The patent changes the approach to handling higher-order filters by using adaptive estimation algorithms that efficiently compute only the necessary Volterra kernel coefficients. This parameter change in the computational method allows higher-order accuracy to be achieved with reduced complexity compared to traditional fixed implementations.
Data Source
AI summary
The present invention is a computationally-efficient compensator for removing nonlinear distortion. The compensator operates in a digital post-compensation configuration for linearization of devices or systems such as analog-to-digital converters and RF receiver electronics. The compensator also operates in a digital pre-compensation configuration for linearization of devices or systems such as digital-to-analog converters, RF power amplifiers, and RF transmitter electronics. The adaptive Volterra compensator effectively removes nonlinear distortion in these systems by implementing an adaptive background algorithm to periodically update actual filter coefficients to maintain optimal performance in operating conditions varying over time (e.g., temperature, frequency, signal level, and drift); or both. The xadaptive background algorithm calculates the optimal nonlinear filter coefficients to reduce nonlinear distortion.