229 citations · 510 across the 30 of their papers we have counts for
39 papers
Exact Gradient Computation for Spiking Neural Networks Through Forward Propagation
Jane H. Lee, Saeid Haghighatshoar, Amin Karbasi
Spiking neural networks (SNN) have recently emerged as alternatives to traditional neural networks, owing to energy efficiency benefits and capacity to better capture biological ne…
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…
Structured Channel Covariance Estimation from Limited Samples for Large Antenna Arrays
Tianyu Yang, Mahdi Barzegar Khalilsarai, Saeid Haghighatshoar +1
In massive multiple-input multiple-output (MIMO) systems, the knowledge of the users' channel covariance matrix is crucial for minimum mean square error (MMSE) channel estimation i…
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…
Machine Learning for Geometrically-Consistent Angular Spread Function Estimation in Massive MIMO
Yi Song, Mahdi Barzegar Khalilsarai, Saeid Haghighatshoar +1
In the spatial channel models used in multi-antenna wireless communications, the propagation from a single-antenna transmitter (e.g., a user) to an M-antenna receiver (e.g., a Base…
Non-Bayesian Activity Detection, Large-Scale Fading Coefficient Estimation, and Unsourced Random Access with a Massive MIMO Receiver
Alexander Fengler, Saeid Haghighatshoar, Peter Jung +1
In this paper, we study the problem of user activity detection and large-scale fading coefficient estimation in a random access wireless uplink with a massive MIMO base station wit…
Massive MIMO Unsourced Random Access
Alexander Fengler, Giuseppe Caire, Peter Jung +1
We consider an extension of the massive unsourced random access originally proposed by Polyanskiy to the case where the receiver has a very large number of antennas (a massive MIMO…
Multiple Measurement Vectors Problem: A Decoupling Property and its Applications
Saeid Haghighatshoar, Giuseppe Caire
We study a Compressed Sensing (CS) problem known as Multiple Measurement Vectors (MMV) problem, which arises in joint estimation of multiple signal realizations when the signal sam…
Efficient Beam Alignment for mmWave Single-Carrier Systems with Hybrid MIMO Transceivers
Xiaoshen Song, Saeid Haghighatshoar, Giuseppe Caire
Communication at millimeter wave (mmWave) bands is expected to become a key ingredient of next generation (5G) wireless networks. Effective mmWave communications require fast and r…
Theoretical Bounds on MAP Estimation in Distributed Sensing Networks
Ali Bereyhi, Saeid Haghighatshoar, Ralf R. Müller
The typical approach for recovery of spatially correlated signals is regularized least squares with a coupled regularization term. In the Bayesian framework, this algorithm is seen…
Hierarchical Sparse Channel Estimation for Massive MIMO
Gerhard Wunder, Ingo Roth, Axel Flinth +4
The problem of wideband massive MIMO channel estimation is considered. Targeting for low complexity algorithms as well as small training overhead, a compressive sensing (CS) approa…
FDD Massive MIMO via UL/DL Channel Covariance Extrapolation and Active Channel Sparsification
Mahdi Barzegar Khalilsarai, Saeid Haghighatshoar, Xinping Yi +1
We propose a novel method for massive Multiple-Input Multiple-Output (massive MIMO) in Frequency Division Duplexing (FDD) systems. Due to the large frequency separation between Upl…
A New Scaling Law for Activity Detection in Massive MIMO Systems
Saeid Haghighatshoar, Peter Jung, Giuseppe Caire
In this paper, we study the problem of \textit{activity detection} (AD) in a massive MIMO setup, where the Base Station (BS) has antennas. We consider a block fading chan…
Multi-Band Covariance Interpolation with Applications in Massive MIMO
Saeid Haghighatshoar, Mahdi Barzegar Khalilsarai, Giuseppe Caire
In this paper, we study the problem of multi-band (frequency-variant) covariance interpolation with a particular emphasis towards massive MIMO applications. In a massive MIMO syste…
A Robust Time-Domain Beam Alignment Scheme for Multi-User Wideband mmWave Systems
Xiaoshen Song, Saeid Haghighatshoar, Giuseppe Caire
Millimeter wave (mmWave) communication with large array gains is a key ingredient of next generation (5G) wireless networks. Effective communication in mmWaves usually depends on t…
Estimation of Angles of Arrival Through Superresolution -- A Soft Recovery Approach for General Antenna Geometries
Mahdi Barzegar, Guiseppe Caire, Axel Flinth +3
The estimation of direction of arrivals with help of -minimization is studied. Contrary to prior work in this direction, which has only considered certain antenna placement des…
Low-Complexity Statistically Robust Precoder/Detector Computation for Massive MIMO Systems
Mahdi Nouri Boroujerdi, Saeid Haghighatshoar, Giuseppe Caire
Massive MIMO is a variant of multiuser MIMO in which the number of antennas at the base station (BS) is very large and typically much larger than the number of served users (da…
FDD Massive MIMO: Efficient Downlink Probing and Uplink Feedback via Active Channel Sparsification
Mahdi Barzegar Khalilsarai, Saeid Haghighatshoar, Xinping Yi +1
In this paper, we propose a novel method for efficient implementation of a massive Multiple-Input Multiple-Output (massive MIMO) system with Frequency Division Duplexing (FDD) oper…
Efficient Downlink Channel Probing and Uplink Feedback in FDD Massive MIMO Systems
Mahdi Barzegar Khalilsarai, Saeid Haghighatshoar, Giuseppe Caire
Massive Multiple-Input Multiple-Output (massive MIMO) is a variant of multi-user MIMO in which the number of antennas at each Base Station (BS) is very large and typically much lar…
A Scalable and Statistically Robust Beam Alignment Technique for mm-Wave Systems
Xiaoshen Song, Saeid Haghighatshoar, Giuseppe Caire
Millimeter-Wave (mm-Wave) frequency bands provide an opportunity for much wider channel bandwidth compared with the traditional sub-6 GHz band. Communication at mm-Waves is, howeve…