Waveform-Adaptive Digital Predistortion for Multi-Signal Linearization
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Solution Overview
Problem
Existing digital predistortion (DPD) systems face challenges in managing signal dynamics for various input waveforms, leading to inefficiencies in power amplifier linearization and spectral regrowth, particularly in wireless basestations where different waveforms require tailored predistortion to avoid interference and meet spectral emission masks.
Innovation Solution
A DPD system comprising a signal classifier block, delay block, and DPD engine that classifies input signals, generates configuration information, and updates predistortion coefficients using feedback, allowing for predistortion of input signals based on modulation type, bandwidth, and power level, with lookup tables segregating coefficients by modulation, bandwidth, and power level to apply appropriate filter coefficients for nonlinear filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single DPD system is used for multiple waveforms, then device complexity is reduced, but predistortion performance deteriorates due to inability to manage signal dynamics for different waveforms
Solution Approach 1:
The DPD system is designed with multi-functionality to handle multiple waveform types (e.g., LTE, WCDMA, GSM) through a unified architecture. The signal classifier identifies different waveform types and routes them to appropriate processing paths, while the lookup tables store predistortion coefficients for various modulation schemes and power levels, enabling a single device to perform multiple predistortion functions without requiring separate dedicated systems for each waveform
2Reliability
If waveform-specific DPD processing is implemented, then predistortion performance is improved, but device complexity increases due to need for separate processing paths
Solution Approach 1:
The DPD system employs dynamic adaptability through a signal classifier that identifies waveform characteristics in real-time and dynamically configures the processing parameters. The lookup tables are organized to accommodate different modulation types, bandwidths, and power levels, allowing the system to dynamically select appropriate predistortion coefficients based on the detected signal characteristics, thereby achieving waveform-specific performance without requiring fixed separate processing paths
3Measurement precision
If lookup tables are segregated by modulation type, bandwidth, and power level, then predistortion precision is improved, but memory requirements and system complexity increase
Solution Approach 1:
The lookup tables are segmented into multiple organized sections based on modulation type, bandwidth, and power level categories. This segmentation allows the system to store precise predistortion coefficients for different signal conditions in structured memory regions, enabling quick identification and retrieval of appropriate coefficients through the signal classifier without requiring a single large unorganized memory structure, thus balancing precision with manageable complexity
Data Source
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AI summary
An apparatus (100), as well as associated system (200) and method (500) therefor, relates generally to signal preconditioning. In such an apparatus (100), a signal classifier block (110) and a delay block (114) are commonly coupled for receiving an input signal (101). The delay block (114) is for providing a delayed version (105) of the input signal (101). The signal classifier block (110) is for providing a configuration signal (107) identifying classification of the input signal (101). A digital predistortion ("DPD") engine (116) is for receiving the delayed version (105) of the input signal (101), a feedback signal (108) and the configuration signal (107) for providing a predistorted version of the input signal (101) as a predistorted output signal (102). The feedback signal (108) has predistortion coefficient adaptation information.