activity
20182023
most citedSparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning

32 citations · 39 across the 7 of their papers we have counts for

collaborators

16 papers

cs.IT2023

Cooperative RIS and STAR-RIS assisted mMIMO Communication: Analysis and Optimization

Anastasios Papazafeiropoulos, Ahmet M. Elbir, Pandelis Kourtessis +2

Reconfigurable intelligent surface (RIS) has emerged as a cost-effective and promising solution to extend the wireless signal coverage and improve the performance via passive signa…

physics.app-ph20223 cited

Machine Learning for Metasurfaces Design and Their Applications

Kumar Vijay Mishra, Ahmet M. Elbir, Amir I. Zaghloul

Metasurfaces (MTSs) are increasingly emerging as enabling technologies to meet the demands for multi-functional, small form-factor, efficient, reconfigurable, tunable, and low-cost…

cs.IT20211 cited

Asymptotic Analysis of Max-Min Weighted SINR for IRS-Assisted MISO Systems with Hardware Impairments

Anastasios Papazafeiropoulos, Cunhua Pan, Ahmet Elbir +3

We focus on the realistic maximization of the uplink minimum signal-to-interference-plus-noise ratio (SINR) of a general multiple-input single-output (MISO) system assisted by an i…

eess.SP2021

Federated Dropout Learning for Hybrid Beamforming With Spatial Path Index Modulation In Multi-User mmWave-MIMO Systems

Ahmet M. Elbir, Sinem Coleri, Kumar Vijay Mishra

Millimeter wave multiple-input multiple-output (mmWave-MIMO) systems with small number of radio-frequency (RF) chains have limited multiplexing gain. Spatial path index modulation…

eess.SP2021

Federated Learning for Physical Layer Design

Ahmet M. Elbir, Anastasios K. Papazafeiropoulos, Symeon Chatzinotas

Model-free techniques, such as machine learning (ML), have recently attracted much interest towards the physical layer design, e.g., symbol detection, channel estimation, and beamf…

cs.LG2020

Hybrid Federated and Centralized Learning

Ahmet M. Elbir, Sinem Coleri, Kumar Vijay Mishra

Many of the machine learning (ML) tasks are focused on centralized learning (CL), which requires the transmission of local datasets from the clients to a parameter server (PS) lead…