Search Results for author: Andrew Hredzak

Found 9 papers, 2 papers with code

DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset

no code implementations17 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.

Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam Prediction

no code implementations14 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.

Management

Towards Real-World 6G Drone Communication: Position and Camera Aided Beam Prediction

no code implementations24 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.

Position

Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets

no code implementations15 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.

Position

Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence

no code implementations18 Mar 2021 Muhammad Alrabeiah, Umut Demirhan, Andrew Hredzak, Ahmed Alkhateeb

To demonstrate the potential of the proposed framework, a wireless network scenario with two coexisting URLL and eMBB services is considered, and two deep learning algorithms are designed to utilize RGB video frames and predict incoming service type and its request time.

Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements

no code implementations18 Jan 2021 Shunyao Wu, Muhammad Alrabeiah, Andrew Hredzak, Chaitali Chakrabarti, Ahmed Alkhateeb

To evaluate our proposed approach, we build a mmWave communication setup with a moving blockage and collect a dataset of received power sequences.

BIG-bench Machine Learning

ViWi Vision-Aided mmWave Beam Tracking: Dataset, Task, and Baseline Solutions

1 code implementation6 Feb 2020 Muhammad Alrabeiah, Jayden Booth, Andrew Hredzak, Ahmed Alkhateeb

These capabilities have the potential of reliably supporting highly-mobile applications such as vehicular/drone communications and wireless virtual/augmented reality in mmWave and terahertz systems.

Millimeter Wave Base Stations with Cameras: Vision Aided Beam and Blockage Prediction

1 code implementation14 Nov 2019 Muhammad Alrabeiah, Andrew Hredzak, Ahmed Alkhateeb

This paper investigates a novel research direction that leverages vision to help overcome the critical wireless communication challenges.

Information Theory Signal Processing Information Theory

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