no code implementations • 20 Aug 2023 • Tawfik Osman, Gouranga Charan, Ahmed Alkhateeb
The developed solution is evaluated on a real-world multi-modal mmWave V2V communication dataset comprising co-existing 360 camera and mmWave beam training data.
no code implementations • 14 Aug 2023 • Gouranga Charan, Muhammad Alrabeiah, Tawfik Osman, Ahmed Alkhateeb
The solutions developed so far, however, have mainly considered single-candidate scenarios, i. e., scenarios with a single candidate user in the visual scene, and were evaluated using synthetic datasets.
no code implementations • 26 Jan 2023 • Ahmed Alkhateeb, Shuaifeng Jiang, Gouranga Charan
This article presents a vision where \textit{real-time} digital twins of the physical wireless environments are continuously updated using multi-modal sensing data from the distributed infrastructure and user devices, and are used to make communication and sensing decisions.
no code implementations • 17 Nov 2022 • Ahmed Alkhateeb, Gouranga Charan, Tawfik Osman, Andrew Hredzak, João Morais, Umut Demirhan, Nikhil Srinivas
This article presents the DeepSense 6G dataset, which is a large-scale dataset based on real-world measurements of co-existing multi-modal sensing and communication data.
no code implementations • 14 Nov 2022 • Gouranga Charan, Andrew Hredzak, Ahmed Alkhateeb
Millimeter wave (mmWave) and terahertz (THz) drones have the potential to enable several futuristic applications such as coverage extension, enhanced security monitoring, and disaster management.
no code implementations • 27 Oct 2022 • Gouranga Charan, Ahmed Alkhateeb
In this paper, we define the \textit{user identification} task as a key enabler for realistic vision-aided communication systems that can operate in crowded scenarios and support multi-user applications.
no code implementations • 15 Sep 2022 • Gouranga Charan, Umut Demirhan, João Morais, Arash Behboodi, Hamed Pezeshki, Ahmed Alkhateeb
In this paper, along with the detailed descriptions of the problem statement and the development dataset, we provide a baseline solution that utilizes the user position data to predict the optimal beam indices.
no code implementations • 24 May 2022 • Gouranga Charan, Andrew Hredzak, Christian Stoddard, Benjamin Berrey, Madhav Seth, Hector Nunez, Ahmed Alkhateeb
Millimeter-wave (mmWave) and terahertz (THz) communication systems typically deploy large antenna arrays to guarantee sufficient receive signal power.
no code implementations • 10 Mar 2022 • Shuaifeng Jiang, Gouranga Charan, Ahmed Alkhateeb
A machine learning (ML) model that leverages the LiDAR sensory data to predict the current and future beams was developed.
no code implementations • 3 Mar 2022 • Gouranga Charan, Ahmed Alkhateeb
This paper provides the first real-world evaluation of using visual (RGB camera) data and machine learning for proactively predicting millimeter wave (mmWave) dynamic link blockages before they happen.
no code implementations • 15 Nov 2021 • Gouranga Charan, Tawfik Osman, Andrew Hredzak, Ngwe Thawdar, Ahmed Alkhateeb
Enabling highly-mobile millimeter wave (mmWave) and terahertz (THz) wireless communication applications requires overcoming the critical challenges associated with the large antenna arrays deployed at these systems.
1 code implementation • 18 Feb 2021 • Gouranga Charan, Muhammad Alrabeiah, Ahmed Alkhateeb
This paper presents a complete machine learning framework for enabling proaction in wireless networks relying on visual data captured, for example, by RGB cameras deployed at the base stations.
no code implementations • 17 Jun 2020 • Gouranga Charan, Muhammad Alrabeiah, Ahmed Alkhateeb
Unlocking the full potential of millimeter-wave and sub-terahertz wireless communication networks hinges on realizing unprecedented low-latency and high-reliability requirements.
no code implementations • 28 May 2019 • Xiaocong Du, Gouranga Charan, Frank Liu, Yu Cao
Such a system requires learning from the data stream, training the model to preserve previous information and adapt to a new task, and generating a single-headed vector for future inference.
no code implementations • 28 May 2019 • Xiaocong Du, Gokul Krishnan, Abinash Mohanty, Zheng Li, Gouranga Charan, Yu Cao
Machine learning algorithms have made significant advances in many applications.