This title slide presents the main title "RF Characterization using SVGP, Composite Kernels, and Active Learning." The subtitle specifies it as a "Thesis Survey Presentation | [Your Name] | [Date]."

RF Characterization using SVGP, Composite Kernels, and Active Learning

Thesis Survey Presentation | [Your Name] | [Date]

This agenda slide outlines a presentation on RF characterization using Sparse Variational Gaussian Processes (SVGP). It covers RF systems overview, SVGP basics, composite kernels for signal modeling, active learning integration, and challenges with conclusion.

Presentation Agenda

1. Introduction to RF Characterization

Overview of RF systems and challenges.

2. SVGP Basics

Fundamentals of Sparse Variational GPs.

3. Composite Kernels

Kernel designs for RF signal modeling.

4. Active Learning Integration

Efficient data selection with SVGP.

5. Challenges and Conclusion

Open issues, applications, and summary. Source: Survey paper on RF Characterization using SVGP, Composite Kernels, and Active Learning.

This slide serves as the section header for "RF Characterization Overview" (Section 02). It highlights key challenges in RF signal modeling and measurement within wireless systems.

RF Characterization Overview

02

RF Characterization Overview

Key challenges in RF signal modeling and measurement in wireless systems.

Source: Thesis Survey: RF Characterization using SVGP, Composite Kernels, and Active Learning

SVGP fundamentals employ sparse inducing points to approximate the full Gaussian Process posterior, using variational inference to maximize the ELBO for scalability. This approach handles large datasets at O(M³) complexity, retains Bayesian nonparametric uncertainty, and enables efficient RF modeling on big data.

SVGP Fundamentals

• Sparse inducing points approximate full GP posterior
• Variational inference maximizes ELBO for scalability
• Handles large datasets with O(M^3) complexity
• Retains Bayesian nonparametric uncertainty quantification
• Enables efficient RF modeling on big data

Composite kernels combine base kernels additively or multiplicatively to enable complex, hierarchical, and multi-scale modeling. They excel at capturing RF signal distortions and memory effects, boosting SVGP accuracy in characterization and outperforming single kernels in active learning.

Composite Kernels

Source: RF Characterization Survey*

The Active Learning Pipeline initializes by training an SVGP model on a seed RF dataset using composite kernels. It then iteratively queries informative unlabeled points with acquisition functions (e.g., Entropy, BALD), acquires RF measurements, and updates the model via variational inference until convergence.

Active Learning Pipeline

{ "headers": [ "Step", "Description", "Key Methods" ], "rows": [ [ "1. Initialization", "Train initial SVGP model on seed RF dataset", "SVGP, Composite Kernels" ], [ "2. Query Strategy", "Select most informative unlabeled RF data points", "Acquisition Functions (e.g., Entropy, BALD)" ], [ "3. Data Selection & Acquisition", "Acquire measurements for queried RF data", "RF Measurement Setup" ], [ "4. Model Update & Loop", "Incorporate new data into SVGP and iterate until convergence", "Variational Inference, Loop back to Query" ] ] }

Source: Thesis Survey: RF Characterization using SVGP, Composite Kernels, and Active Learning

The "Integration Benefits" slide showcases SVGP's advantages for RF datasets via a feature grid, highlighting scalable computation, uncertainty quantification, reduced experiments, composite kernels, data efficiency, and fast inference. These enable efficient handling of large datasets, probabilistic predictions, minimized testing, complex signal capture, sparse data modeling, and rapid RF characterization.

Integration Benefits

{ "features": [ { "icon": "🚀", "heading": "Scalable Computation", "description": "SVGP handles large RF datasets efficiently without performance loss." }, { "icon": "🎯", "heading": "Uncertainty Quantification", "description": "Provides probabilistic predictions with reliable confidence intervals." }, { "icon": "🔬", "heading": "Reduced Experiments", "description": "Active learning minimizes costly RF tests by selecting key points." }, { "icon": "🔗", "heading": "Composite Kernels", "description": "Captures complex multi-scale behaviors in RF signals." }, { "icon": "💡", "heading": "Data Efficiency", "description": "Learns accurate models from sparse experimental data." }, { "icon": "⚡", "heading": "Fast Inference", "description": "Sparse methods enable rapid predictions for RF characterization." } ] }

The conclusion slide states that combining SVGP, Kernels, and AL revolutionizes RF characterization. It identifies real-world validation as the next step.

Conclusion & Future Work

SVGP + Kernels + AL revolutionizes RF characterization.

Next: Real-world validation.

Revolutionizing RF Char. | Validate in real-world now!

Source: RF Characterization Thesis Survey