4 citations · 5 across the 4 of their papers we have counts for
5 papers
Machine Learning-based Methods for Reconfigurable Antenna Mode Selection in MIMO Systems
Yasaman Abdollahian, Ehsan Tohidi, Martin Kasparick +3
MIMO technology has enabled spatial multiple access and has provided a higher system spectral efficiency (SE). However, this technology has some drawbacks, such as the high number…
MIMO Systems with Reconfigurable Antennas: Joint Channel Estimation and Mode Selection
Fariba Armandoust, Ehsan Tohidi, Martin Kasparick +3
Reconfigurable antennas (RAs) are a promising technology to enhance the capacity and coverage of wireless communication systems. However, RA systems have two major challenges: (i)…
Hybrid Model and Data Driven Algorithm for Online Learning of Any-to-Any Path Loss Maps
M. A. Gutierrez-Estevez, Martin Kasparick, Renato L. G. Cavalvante +1
Learning any-to-any (A2A) path loss maps, where the objective is the reconstruction of path loss between any two given points in a map, might be a key enabler for many applications…
Transfer Learning in Multi-Agent Reinforcement Learning with Double Q-Networks for Distributed Resource Sharing in V2X Communication
Hammad Zafar, Zoran Utkovski, Martin Kasparick +1
This paper addresses the problem of decentralized spectrum sharing in vehicle-to-everything (V2X) communication networks. The aim is to provide resource-efficient coexistence of ve…
Leveraging Machine Learning for Industrial Wireless Communications
Ilaria Malanchini, Patrick Agostini, Khurshid Alam +10
Two main trends characterize today's communication landscape and are finding their way into industrial facilities: the rollout of 5G with its distinct support for vertical industri…