Support Vector Machine-Based Model for 2.5-5.2 GHz CMOS Power Amplifier.

Area of Science:

• Electrical Engineering
• Wireless Communication Systems
• Machine Learning Applications

Background:

• Power amplifiers (PAs) are critical components in wireless communication systems, directly impacting system performance and stability.
• PA specifications are sensitive to temperature variations, necessitating accurate modeling for reliable operation.
• Optimizing the amount of experimental data for Support Vector Machine (SVM) modeling is crucial for efficiency.

Purpose of the Study:

• To investigate the impact of varying training data quantities on SVM model accuracy for PA temperature characteristics.
• To determine the minimum experimental data required for effective PA specification degradation modeling.
• To reduce measurement costs and time in PA performance evaluation.

Main Methods:

• Utilized Support Vector Machine (SVM) to model the temperature-dependent characteristics of power amplifiers.
• Systematically varied the percentage of experimental data used for SVM model training.
• Evaluated model performance based on the amount of training data employed.

Main Results:

• Achieved satisfactory modeling of PA temperature characteristics using only 75% of the available experimental data.
• Demonstrated that 25% of measurement points in PA specification degradation experiments can be reduced without compromising model requirements.
• Identified an optimal data subset for SVM modeling that meets performance criteria.

Conclusions:

• Reduced experimental data requirements for PA modeling using SVM, leading to significant cost and time savings.
• The study provides a practical guideline for optimizing experimental design in wireless communication system testing.
• Efficient data utilization in SVM modeling enhances the feasibility of characterizing PA degradation under temperature variations.