AI-Based Distortion Compensation for RF Chain Circuitry
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Solution Overview
Problem
Wireless communications systems face challenges such as signal attenuation and distortion due to complex and dynamic environments, leading to inefficiencies in data transmission and reception.
Innovation Solution
The implementation of an AI-based method for calibrating distortion compensation in RF chain circuitry, where an AI model is trained to predict filter parameters to suppress distortion, using input data from calibration signals with tones in a specific frequency bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional calibration methods are used to compensate for distortion, then distortion compensation can be achieved, but the process is time-consuming and computationally intensive
Solution Approach 1:
The patent replaces traditional mechanical/mathematical calibration processes with an AI-based system that uses machine learning models to predict and compensate for distortion. The AI model is trained on calibration signals and can rapidly determine compensation parameters without time-consuming iterative calculations, thus reducing calibration time while maintaining compensation effectiveness.
Solution Approach 2:
The patent changes the approach from using fixed calibration parameters to dynamically determined AI model parameters. The AI model processes calibration signals and outputs optimized compensation parameters that adapt to different distortion conditions, enabling faster and more flexible calibration compared to traditional fixed-parameter methods.
2Reliability
If traditional distortion compensation techniques are applied, then signal distortion can be reduced, but processing complexity and computational resources increase
Solution Approach 1:
The patent substitutes complex mathematical distortion compensation algorithms with an AI-based system. The AI model, once trained, can rapidly process calibration signals and determine compensation parameters through pattern recognition rather than complex calculations, reducing processing complexity while maintaining or improving compensation effectiveness.
Solution Approach 2:
The patent uses AI models that have learned distortion patterns from training data. Instead of computing compensation in real-time through complex algorithms, the system copies learned compensation strategies from the trained AI model, enabling rapid and efficient distortion compensation with lower computational requirements.
3Measurement precision
If extensive calibration signals are used to ensure accurate distortion compensation, then compensation accuracy improves, but the frequency bandwidth and processing load increase
Solution Approach 1:
The patent changes the approach from using extensive calibration signals across broad frequency bands to a targeted AI-based calibration process. The AI model processes calibration signals efficiently and can generalize distortion compensation across the operational bandwidth, achieving accurate compensation without requiring proportional increases in calibration signal quantity or bandwidth.
Solution Approach 2:
The patent applies partial calibration using a limited set of calibration signals, and the AI model fills in the remaining compensation requirements through learned patterns. This partial action approach reduces the quantity of calibration signals needed while still achieving comprehensive distortion compensation across the frequency bandwidth.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for artificial intelligence based calibration of distortion compensation for radio frequency chain circuitry. An example method for wireless communications includes providing, to at least one artificial intelligence (AI) model, first input based at least in part on at least one output signal corresponding to at least one calibration signal having one or more tones in a frequency bandwidth. The method further includes obtaining, from the at least one AI model, first output comprising an indication of one or more filter parameters configured to suppress distortion in the frequency bandwidth. The method further includes storing the one or more filter parameters in one or more memories.