74 citations · 97 across the 14 of their papers we have counts for
25 papers
EXODUS: Stable and Efficient Training of Spiking Neural Networks
Felix Christian Bauer, Gregor Lenz, Saeid Haghighatshoar +1
Spiking Neural Networks (SNNs) are gaining significant traction in machine learning tasks where energy-efficiency is of utmost importance. Training such networks using the state-of…
Dual-Polarized FDD Massive MIMO: A Comprehensive Framework
Mahdi Barzegar Khalilsarai, Tianyu Yang, Saeid Haghighatshoar +2
We propose a comprehensive scheme for realizing a massive multiple-input multiple-output (MIMO) system with dual-polarized antennas in frequency division duplexing (FDD) mode. Empl…
Joint Approximate Covariance Diagonalization with Applications in MIMO Virtual Beam Design
Mahdi Barzegar Khalilsarai, Saeid Haghighatshoar, Giuseppe Caire
We study the problem of maximum-likelihood (ML) estimation of an approximate common eigenstructure, i.e. an approximate common eigenvectors set (CES), for an ensemble of covariance…
Uplink-Downlink Channel Covariance Transformations and Precoding Design for FDD Massive MIMO
Mahdi Barzegar Khalilsarai, Yi Song, Tianyu Yang +2
A large majority of cellular networks deployed today make use of Frequency Division Duplexing (FDD) where, in contrast with Time Division Duplexing (TDD), the channel reciprocity d…
Grant-Free Massive Random Access With a Massive MIMO Receiver
Alexander Fengler, Saeid Haghighatshoar, Peter Jung +1
We consider the problem of unsourced random access (U-RA), a grant-free uncoordinated form of random access, in a wireless channel with a massive MIMO base station equipped with a…
Structured Channel Covariance Estimation from Limited Samples in Massive MIMO
Mahdi Barzegar Khalilsarai, Tianyu Yang, Saeid Haghighatshoar +1
Obtaining channel covariance knowledge is of great importance in various Multiple-Input Multiple-Output MIMO communication applications, including channel estimation and covariance…