5 citations · 11 across the 15 of their papers we have counts for
6 papers · 1 filter
Fusion of Cellular ISAC and Passive RF Sensing for UAV Detection and Tracking
Cole Dickerson, Sean Kearney, Sultan Manjur +5
The rapid growth of unmanned aerial vehicles (UAVs) in civilian and critical-infrastructure airspace has created a need for reliable detection and tracking systems that operate und…
Elevation- and Tilt-Aware Shadow Fading Correlation Modeling for UAV Communications
Mushfiqur Rahman, Ismail Guvenc, Mihail Sichitiu +4
Future wireless networks demand a more accurate understanding of channel behavior to enable efficient communication with reduced interference. Uncrewed Aerial Vehicles (UAVs) are p…
Energy Efficient Sleep Mode Optimization in 5G mmWave Networks via Multi Agent Deep Reinforcement Learning
Saad Masrur, Ismail Guvenc, David Lopez Perez
Dynamic sleep mode optimization (SMO) in millimeter-wave (mmWave) networks is essential for maximizing energy efficiency (EE) under stringent quality-of-service (QoS) constraints.…
UAV-Assisted Coverage Hole Detection Using Reinforcement Learning in Urban Cellular Networks
Mushfiqur Rahman, Ismail Guvenc, David Ramirez +1
Deployment of cellular networks in urban areas requires addressing various challenges. For example, high-rise buildings with varying geometrical shapes and heights contribute to si…
Impact of Altitude, Bandwidth, and NLOS Bias on TDOA-Based 3D UAV Localization: Experimental Results and CRLB Analysis
Cole Dickerson, Saad Masrur, Jonah Dickerson +2
This paper investigates unmanned aerial vehicle (UAV) localization using time difference of arrival (TDOA) measurements under mixed line-of-sight (LOS) and non-line-of-sight (NLOS)…
Transforming Indoor Localization: Advanced Transformer Architecture for NLOS Dominated Wireless Environments with Distributed Sensors
Saad Masrur, Jung-Fu, Cheng +2
Indoor localization in challenging non-line-of-sight (NLOS) environments often leads to poor accuracy with traditional approaches. Deep learning (DL) has been applied to tackle the…