DPD Coefficient Estimation for Abrupt Signal Profile Changes
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Solution Overview
Problem
Existing digital pre-distortion (DPD) coefficient estimation systems fail to accurately track abrupt changes in signal profiles, leading to performance degradation due to non-linearity in power amplifier (PA) circuitry, which results in increased adjacent channel leakage ratio (ACLR) and error vector magnitude (EVM) in output signals.
Innovation Solution
Implement a DPD coefficient estimation system that includes a profile agnostic coefficient set (PACS) generator and a profile change detector to dynamically adjust coefficients based on signal profile changes, using a regularization technique to ensure convergence across varying signal conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional DPD coefficient estimation systems use fixed coefficients, then the system structure remains simple, but the system cannot track abrupt changes in signal profiles leading to performance degradation
Solution Approach 1:
The system dynamically switches between different coefficient sets (first, second, and third sets) based on detected signal profile changes. The DPD corrector adapts its coefficients in real-time according to the current signal profile, transforming a static system into a dynamic one that responds to changing conditions without requiring complete re-estimation.
Solution Approach 2:
Multiple coefficient sets are pre-computed and stored for different signal profiles before operation. When a profile change is detected, the system can immediately switch to the appropriate pre-computed coefficient set or generate a third set based on stored data, avoiding the need for time-consuming real-time coefficient estimation from scratch.
2Measurement precision
If the system updates coefficients frequently to track profile changes, then tracking accuracy improves, but computational overhead and processing time increase
Solution Approach 1:
The system maintains different coefficient sets optimized for different local signal profile conditions. Instead of using a single global coefficient set, it stores multiple specialized sets (first set for first profile, second set for second profile) and selects the appropriate local optimum based on the current signal conditions, achieving high accuracy without continuous re-optimization.
Solution Approach 2:
The system creates a third coefficient set by combining information from the first and second coefficient sets when profile changes are detected. This copying and merging approach allows rapid generation of appropriate coefficients based on existing stored data, avoiding time-consuming new estimations while maintaining accuracy.
3Reliability
If the system uses separate coefficient sets for different profiles, then performance across varying profiles improves, but memory requirements and system resources increase
Solution Approach 1:
The third coefficient set serves multiple purposes: it handles transition periods between profiles, provides a fallback when profile detection is uncertain, and can be used as a basis for future coefficient generation. This multi-functional approach allows the system to maintain high reliability across varying profiles while managing memory resources efficiently through intelligent coefficient set management.
4Productivity
If the system implements real-time profile detection and coefficient switching, then adaptability to signal changes improves, but processing complexity and computational load increase
Solution Approach 1:
The system merges the functionality of multiple separate coefficient estimation systems into a unified architecture that manages multiple coefficient sets through a single control logic. The profile detector and coefficient selector are integrated to work together, reducing overall system complexity compared to having independent estimation systems for each profile while maintaining fast response capability.
Data Source
AI summary
In an embodiment, a method includes: providing a first set of coefficients to a digital pre-distortion (DPD) corrector, the DPD corrector receiving a input signal having a first profile, the first set of coefficients being associated with the first profile; in response to detecting a change in a profile of the input signal from the first profile to a second profile, extracting, in response to an output signal converging to the input signal, a second set of coefficients corresponding to the second profile, the output signal being based on an output of the DPD corrector; and generating a third set of coefficients based on the first and second sets of coefficients, the third set being different from the first and second sets.