activity
20122023
most citedCovariance-Based OFDM Spectrum Sensing with Sub-Nyquist Samples

17 citations · 20 across the 8 of their papers we have counts for

collaborators

8 papers

eess.SP2023

Structured Two-Stage True-Time-Delay Array Codebook Design for Multi-User Data Communication

Aditya Wadaskar, Ding Zhao, Ibrahim Pehlivan +1

Wideband millimeter-wave and terahertz (THz) systems can facilitate simultaneous data communication with multiple spatially separated users. It is desirable to orthogonalize users…

eess.SP2023

Deep Learning Based Active Spatial Channel Gain Prediction Using a Swarm of Unmanned Aerial Vehicles

Enes Krijestorac, Danijela Cabric

Prediction of wireless channel gain (CG) across space is a necessary tool for many important wireless network design problems. In this paper, we develop prediction methods that use…

eess.SP20231 cited

RadYOLOLet: Radar Detection and Parameter Estimation Using YOLO and WaveLet

Shamik Sarkar, Dongning Guo, Danijela Cabric

Detection of radar signals without assistance from the radar transmitter is a crucial requirement for emerging and future shared-spectrum wireless networks like Citizens Broadband…

cs.NI2023

REM-U-net: Deep Learning Based Agile REM Prediction with Energy-Efficient Cell-Free Use Case

Hazem Sallouha, Shamik Sarkar, Enes Krijestorac +1

Radio environment maps (REMs) hold a central role in optimizing wireless network deployment, enhancing network performance, and ensuring effective spectrum management. Conventional…

cs.NI2023

ProSpire: Proactive Spatial Prediction of Radio Environment Using Deep Learning

Shamik Sarkar, Dongning Guo, Danijela Cabric

Spatial prediction of the radio propagation environment of a transmitter can assist and improve various aspects of wireless networks. The majority of research in this domain can be…

cs.IT201517 cited

Covariance-Based OFDM Spectrum Sensing with Sub-Nyquist Samples

Alireza Razavi, Mikko Valkama, Danijela Cabric

In this paper, we propose a feature-based method for spectrum sensing of OFDM signals from sub-Nyquist samples over a single band. We exploit the structure of the covariance matrix…