6 papers
Wireless Dataset Similarity: Measuring Distances in Supervised and Unsupervised Machine Learning
João Morais, Sadjad Alikhani, Akshay Malhotra +2
This paper introduces a task- and model-aware framework for measuring similarity between wireless datasets, enabling applications such as dataset selection/augmentation, simulation…
Comparing Stochastic and Ray-tracing Datasets in Machine Learning for Wireless Applications
João Morais, Akshay Malhotra, Shahab Hamidi-Rad +1
Machine learning for wireless systems is commonly studied using standardized stochastic channel models (e.g., TDL/CDL/UMa) because of their legacy in wireless communication standar…
Sculpting Latent Spaces With MMD: Disentanglement With Programmable Priors
Quentin Fruytier, Akshay Malhotra, Shahab Hamidi-Rad +3
Learning disentangled representations, where distinct factors of variation are captured by independent latent variables, is a central goal in machine learning. The dominant approac…
Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad +3
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML s…
Physics-Informed Generative Approaches for Wireless Channel Modeling
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad +3
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML s…
A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing
Joao Morais, Sadjad Alikhani, Akshay Malhotra +2
This paper introduces a task-specific, model-agnostic framework for evaluating dataset similarity, providing a means to assess and compare dataset realism and quality. Such a frame…