Our Research Builds the Next-Generation
AI-Powered Sensing and Communications Systems
Directed by Prof. Ahmed Alkhateeb, the Wireless Intelligence Lab focuses on leveraging signal processing and machine learning to address the key challenges of future wireless communication systems and enable new sensing and positioning capabilities. We develop enabling technologies, mathematically evaluate their theoretical limits, build proof-of-concept hardware prototypes, and demonstrate the developed solutions in real-world environments.
Announcement
We are always looking for highly-motivated Ph.D. students to join our lab. Interested students are encouraged to contact Prof. Alkhateeb with a brief resume.
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Research Highlights
News
Jun. 2025
The paper “Enabling ISAC in Real World: Beam-Based User Identification with Machine Learning” has been accepted to the IEEE Wireless Communications Letters.
Jun. 2025
The paper “DeepSense-V2V: A Vehicle-to-Vehicle Multi-Modal Sensing, Localization, and Communications Dataset” has been accepted to the IEEE Transactions on Vehicular Technology.
Jun. 2025
Ph.D. student Tawfik Osman is interning at Apple in Summer 2025.
Ph.D. student Hao Luo is interning at Ericsson in Summer 2025.
Ph.D. student Sadjad Alikhani is interning at Nokia Bell Labs in Summer 2025.
Feb. 2025
The paper “Learnable Wireless Digital Twins: Reconstructing Electromagnetic Field With Neural Representations” has been accepted to the IEEE Open Journal of the Communications Society.
Jan. 2025
Namhyun Kim and Kengmin Lin started their Ph.D. in Electrical Engineering at Arizona State University and joined the Wireless Intelligence Lab.
Jan. 2025
Dec. 2024
The paper “Environment Semantic Communication: Enabling Distributed Sensing Aided Networks” has been accepted to the IEEE Open Journal of the Communications Society.
Dec. 2024
The paper “Camera Based mmWave Beam Prediction: Towards Multi-Candidate Real-World Scenarios” has been accepted to the IEEE Transactions on Vehicular Technology.
Dec. 2024
Nov. 2024
The paper “Large Wireless Model (LWM): A Foundation Model for Wireless Channels” is available on arXiv. Find more details on the LWM website.
Support
The Wireless Intelligence Lab is grateful for the support it is receiving from the following organizations
