5 papers
LWM-Spectro: A Foundation Model for Wireless Baseband Signal Spectrograms
Namhyun Kim, Sadjad Alikhani, Ahmed Alkhateeb
The received in-phase and quadrature (I/Q) baseband signals inherently encode physical-layer and channel characteristics of wireless links. Learning robust and transferable represe…
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…
Digital Twin Aided Channel Estimation: Zone-Specific Subspace Prediction and Calibration
Sadjad Alikhani, Ahmed Alkhateeb
Effective channel estimation in sparse and high-dimensional environments is essential for next-generation wireless systems, particularly in large-scale MIMO deployments. This paper…
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…
Large Wireless Model (LWM): A Foundation Model for Wireless Channels
Sadjad Alikhani, Gouranga Charan, Ahmed Alkhateeb
This paper presents Large Wireless Model (LWM) -- the world's first foundation model for wireless channels. Designed as a task-agnostic model, LWM generates universal, rich, contex…