Neural Network Predistortion for High-Power Radio Linearity
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Solution Overview
Problem
Conventional radio communication systems face challenges in minimizing distortion and interference while operating amplifiers at maximal output power levels, leading to inefficiencies and increased costs due to the need for careful amplifier design and reduced power consumption.
Innovation Solution
Integration of a nonlinear pre-distortion machine learning model that uses neural networks to correct radio signal distortion and interference by generating pre-distorted signals, allowing amplifiers to operate at maximal power levels while minimizing interference and distortion, and updating model parameters based on environmental conditions and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If amplifiers operate at maximal output power levels, then power efficiency and productivity are improved, but signal distortion and interference increase
Solution Approach 1:
The system applies pre-distortion to the signal before amplification by processing the input signal through a neural network model that predicts and compensates for expected distortion. This preliminary correction allows the amplifier to operate at maximal power levels while the pre-distorted signal already contains compensating errors that will cancel out the amplifier's non-linearities, thereby maintaining both high power efficiency and low distortion
Solution Approach 2:
The system uses feedback mechanisms where the neural network model is trained using received signal data and distance metrics that measure the difference between transmitted and received signals. This feedback loop continuously updates the model parameters to minimize distortion, enabling the amplifier to operate efficiently while maintaining signal integrity through adaptive correction
2Manufacturing precision
If traditional digital pre-distortion methods are used, then some distortion correction is achieved, but linearity and interference removal capacity are insufficient
Solution Approach 1:
The system replaces traditional mathematical pre-distortion algorithms with a neural network-based machine learning model. This substitution enables the system to learn complex non-linear relationships and environmental variations that traditional methods cannot capture, significantly improving linearity and interference removal capacity through adaptive pattern recognition rather than fixed mathematical transformations
Solution Approach 2:
The system dynamically changes model parameters based on environmental conditions by receiving feedback signals and updating neural network weights and biases. This parameter adaptation allows the pre-distortion system to maintain optimal linearity performance across varying operating conditions, frequencies, and environmental factors, overcoming the static nature of traditional pre-distortion methods
3Adaptability or versatility
If model parameters are updated based on environmental conditions, then adaptability and reliability are improved, but system complexity increases
Solution Approach 1:
The system implements self-service through autonomous model parameter updates where the neural network automatically adjusts its weights and biases based on received signal feedback and distance metrics. The system self-tunes to environmental conditions without requiring manual intervention or complex external control mechanisms, thereby improving adaptability while keeping the overall system architecture relatively simple through automated self-optimization
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for correcting distortion of radio signals A transmit radio signal corresponding to an output of a transmitting radio signal processing system is obtained. A pre-distorted radio signal is then generated by processing the transmit radio signal using a nonlinear pre-distortion machine learning model. The nonlinear pre-distortion machine learning model includes model parameters and at least one nonlinear function to correct radio signal distortion or interference. A transmit output radio signal is obtained by processing the pre-distorted radio signal through the transmitting radio signal processing system. The transmit output radio signal is then transmitted to one or more radio receivers.