11 papers
GCNO: Gramian Chebyshev Neural Operator for Physics-Based Compression of Wireless Channels
Rafid Umayer Murshed, Shahab Hamidi-Rad, Elahe Soltanaghai +1
Large antenna arrays allow wireless systems to serve more users and achieve higher data rates, but they also make channel feedback expensive: the receiving device must repeatedly r…
Unlocking Realism and Interpretability in Wireless Channel Synthesis: A Physics-Guided Generative Approach
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad +3
In recent years, machine learning (ML) methods have become increasingly popular for wireless communication systems. These require large amounts of data reflecting the behavior of r…
LWM-CDE: A Representation Space for Wireless Data Reasoning and Transferability
Sadjad Alikhani, Akshay Malhotra, Shahab Hamidi-Rad +1
Machine learning deployments in real-world wireless communication tasks face significant generalization challenges due to location and environment-specific signal structure, high d…
From Fewer Samples to Fewer Bits: Reframing Dataset Distillation as Joint Optimization of Precision and Compactness
My H. Dinh, Aditya Sant, Akshay Malhotra +2
Dataset Distillation (DD) compresses large datasets into compact synthetic ones that maintain training performance. However, current methods mainly target sample reduction, with li…
LWM-Temporal: Sparse Spatio-Temporal Attention for Wireless Channel Representation Learning
Sadjad Alikhani, Akshay Malhotra, Shahab Hamidi-Rad +1
LWM-Temporal is a new member of the Large Wireless Models (LWM) family that targets the spatiotemporal nature of wireless channels. Designed as a task-agnostic foundation model, LW…
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…