Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Krijestorac, Enes"'
Deep Learning Based Active Spatial Channel Gain Prediction Using a Swarm of Unmanned Aerial Vehicles
Autor:
Krijestorac, Enes, Cabric, Danijela
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 environment-specific features, namely building maps and CG measuremen
Externí odkaz:
http://arxiv.org/abs/2310.04547
Radio environment maps (REMs) hold a central role in optimizing wireless network deployment, enhancing network performance, and ensuring effective spectrum management. Conventional REM prediction methods are either excessively time-consuming, e.g., r
Externí odkaz:
http://arxiv.org/abs/2309.11898
Radio frequency fingerprinting has been proposed for device identification. However, experimental studies also demonstrated its sensitivity to deployment changes. Recent works have addressed channel impacts by developing robust algorithms accounting
Externí odkaz:
http://arxiv.org/abs/2303.14312
We consider a novel ultra-narrowband (UNB) low-power wide-area network (LPWAN) architecture design for uplink transmission of a massive number of Internet of Things (IoT) devices over multiple multiplexing bands. An IoT device can randomly choose any
Externí odkaz:
http://arxiv.org/abs/2206.06446
Distributed transmit beamforming enables cooperative radios to act as one virtual antenna array, extending their communications' range beyond the capabilities of a single radio. Most existing distributed beamforming approaches rely on the destination
Externí odkaz:
http://arxiv.org/abs/2108.01837
A swarm of cooperating UAVs communicating with a distant multiantenna ground station can leverage MIMO spatial multiplexing to scale the capacity. Due to the line-of-sight propagation between the swarm and the ground station, the MIMO channel is high
Externí odkaz:
http://arxiv.org/abs/2104.14075
Machine learning (ML) and artificial neural networks (ANNs) have been successfully applied to simulating complex physics by learning physics models thanks to large data. Inspired by the successes of ANNs in physics modeling, we use deep neural networ
Externí odkaz:
http://arxiv.org/abs/2011.03597
Physical layer authentication relies on detecting unique imperfections in signals transmitted by radio devices to isolate their fingerprint. Recently, deep learning-based authenticators have increasingly been proposed to classify devices using these
Externí odkaz:
http://arxiv.org/abs/2011.01538
Autor:
Krijestorac, Enes, Memedi, Agon, Higuchi, Takamasa, Ucar, Seyhan, Altintas, Onur, Cabric, Danijela
In this work, we propose the use of hybrid offloading of computing tasks simultaneously to edge servers (vertical offloading) via LTE communication and to nearby cars (horizontal offloading) via V2V communication, in order to increase the rate at whi
Externí odkaz:
http://arxiv.org/abs/2010.05693
In this work, we consider a novel type of Internet of Things (IoT) ultra-narrowband (UNB) network architecture that involves multiple multiplexing bands or channels for uplink transmission. An IoT device can randomly choose any of the multiplexing ba
Externí odkaz:
http://arxiv.org/abs/2010.04307