Nonlinear RF Pre-Distortion Using Machine Learning Feedback
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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 inefficient design and increased costs due to the need for careful amplifier design for linearity 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 is improved, but signal distortion and interference increase
Solution Approach 1:
The system applies preliminary pre-distortion to the radio signal before it enters the amplifier. The nonlinear pre-distortion machine learning model processes the input signal to pre-compensate for the expected distortion that will occur during amplification, allowing the amplifier to operate at maximal power levels without producing excessive distortion in the final output signal.
Solution Approach 2:
The system implements feedback by receiving the output signal from the amplifier and using it to update the model parameters of the pre-distortion model. This closed-loop approach allows the system to adapt to changing conditions and optimize the pre-distortion compensation, maintaining low distortion while operating at high power efficiency.
2Manufacturing precision
If conventional linear amplifier design is used to minimize distortion, then signal quality is improved, but power consumption increases
Solution Approach 1:
The system replaces the traditional mechanical/approach of designing linear amplifiers with careful circuit design and operating point selection with a machine learning-based pre-distortion approach. The nonlinear pre-distortion model dynamically compensates for distortion, allowing the use of more efficient nonlinear amplifier designs that would otherwise produce unacceptable distortion levels.
3Object-generated harmful factors
If traditional pre-distortion methods are used, then distortion correction is achieved, but computational requirements and complexity increase
Solution Approach 1:
The system uses a machine learning model with learnable parameters that are optimized during training to minimize distortion. Once trained, the model efficiently computes pre-distortion with relatively simple operations during runtime. The model parameters are updated based on feedback from the received signal, allowing adaptation to different operating conditions without increasing structural complexity.
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.