Multi-Band Digital Predistortion With Adaptive Lookup-Table Linearization
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Solution Overview
Problem
Existing methods for linearizing non-linear power amplifiers and radio transmitter chains with multi-band inputs face challenges in reducing computation requirements and storage needs while maintaining robustness against variations in parameter values and system characteristics, leading to suboptimal performance in linearization and increased unwanted emissions between frequency bands.
Innovation Solution
A pre-distorter that uses diverse real-valued signals derived from input signals and their combinations, undergoing configurable non-linear transformations, with outputs serving as gain terms for complex signals, which are summed to compute the pre-distorted signal. This approach involves phase-invariant derived signals and time-varying gains, efficiently implemented using lookup tables, and adapts to sensed output of the transmit chain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional pre-distortion methods are used to linearize non-linear power amplifiers, then linearity is improved, but computation requirements and storage needs increase
Solution Approach 1:
The patent segments the multi-band input signal into multiple separate frequency bands before processing. Each band is handled independently through its own pre-distortion function, reducing the overall computational complexity compared to processing the entire multi-band signal as a single complex input. The segmentation allows for simpler, band-specific lookup tables and parameter sets.
Solution Approach 2:
The patent employs parameterized pre-distortion functions where the transfer characteristics can be adjusted based on the specific frequency band being processed. By changing parameters such as gain, phase, and polynomial coefficients for different bands, the system achieves accurate linearization without requiring overly complex universal models for all frequency ranges.
2Manufacturing precision
If traditional pre-distortion methods are used to linearize non-linear power amplifiers, then linearity is improved, but storage needs increase
Solution Approach 1:
The patent divides the storage requirements into separate, smaller lookup tables for each frequency band rather than requiring one large table for the entire multi-band signal. This segmentation reduces peak memory usage and allows for more efficient storage management.
Solution Approach 2:
The patent implements a reduced set of pre-distortion terms and parameters for each band, using only the most significant terms needed for adequate linearization performance. This partial approach reduces storage requirements while maintaining acceptable linearity, avoiding the need to store and process all possible higher-order terms.
3Device complexity
If simple pre-distortion models are used, then computation requirements are reduced, but robustness against parameter variations deteriorates
Solution Approach 1:
The patent implements dynamic adaptation mechanisms where the pre-distortion parameters are adjusted based on feedback from the actual amplifier output. This allows the relatively simple pre-distortion models to compensate for parameter variations and environmental changes, enhancing robustness without significantly increasing computational complexity.
Solution Approach 2:
The system incorporates feedback loops that monitor the actual output of the power amplifier and use this information to adjust the pre-distortion parameters in real-time. This feedback mechanism enables simple models to maintain robust performance despite variations in amplifier characteristics, temperature, and other environmental factors.
4Quantity of substance
If simple pre-distortion models are used, then storage needs are reduced, but linearization performance deteriorates
Solution Approach 1:
The patent applies local quality by optimizing the pre-distortion parameters and polynomial terms specifically for each frequency band's characteristics. Rather than using a uniform, overly complex model for all bands, each band receives a tailored pre-distortion function with the appropriate level of complexity, achieving good linearization performance with minimal storage requirements for each band.
Data Source
AI summary
A pre-distorter that both accurately compensates for the non-linearities of a radio frequency transmit chain, and that imposes as few computation requirements in terms of arithmetic operations, uses a diverse set of real-valued signals that are derived from separate band signals that make up the input signal. The derived real signals are passed through configurable non-linear transformations, which may be adapted during operation, and which may be efficiently implemented using lookup tables. The outputs of the non-linear transformations serve as gain terms for a set of complex signals, which are functions of the input, and which are summed to compute the pre-distorted signal. A small set of the complex signals and derived real signals may be selected for a particular system to match the classes of non-linearities exhibited by the system, thereby providing further computational savings, and reducing complexity of adapting the pre-distortion through adapting of the non-linear transformations.