Nonlinear RF Pre-Distortion Using Machine Learning for Amplifier 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 or near-maximal power levels while minimizing interference and distortion, and adapting to environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If amplifiers operate at maximal output power levels, then power efficiency improves, but signal distortion and interference increase
Solution Approach 1:
The system applies pre-distortion to the signal before amplification, intentionally distorting the signal in advance so that the amplifier's nonlinearity cancels out the pre-applied distortion. This preliminary action allows the amplifier to operate at maximal power levels without producing harmful distortion in the final output signal.
Solution Approach 2:
The system dynamically adjusts model parameters based on environmental conditions and amplifier characteristics. By changing the parameters of the pre-distortion model, the system optimizes the correction applied to the signal, enabling maximal power operation while maintaining signal quality across varying conditions.
2Manufacturing precision
If traditional digital pre-distortion methods are used, then signal linearity improves, but computational complexity and power consumption increase
Solution Approach 1:
The system replaces complex traditional digital pre-distortion algorithms with a machine learning-based model that has been trained offline. This substitution reduces real-time computational complexity while maintaining signal linearity, as the heavy computational burden is shifted to the training phase rather than the operational phase.
Solution Approach 2:
The system uses a trained machine learning model that captures the amplifier's nonlinear characteristics. This model serves as a simplified copy or representation of the complex amplifier behavior, enabling efficient real-time pre-distortion without requiring complex real-time calculations.
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.