5 citations · 10 across the 16 of their papers we have counts for
16 papers · 1 filter
Downlink CSIT under Compressed Feedback: Joint vs. Separate Source-Channel Coding
Yi Song, Tianyu Yang, Mahdi Barzegar Khalilsarai +1
The acquisition of Downlink (DL) channel state information at the transmitter (CSIT) is known to be a challenging task in multiuser massive MIMO systems when uplink/downlink channe…
Deep-Learning Aided Channel Training and Precoding in FDD Massive MIMO with Channel Statistics Knowledge
Yi Song, Tianyu Yang, Mahdi Barzegar Khalilsarai +1
We propose a method for channel training and precoding in FDD massive MIMO based on deep neural networks (DNNs), exploiting Downlink (DL) channel covariance knowledge. The DNN is o…
FDD Massive MIMO Channel Training Optimal Rate Distortion Bounds and the Efficiency of one-shot Schemes
Mahdi Barzegar Khalilsarai, Yi Song, Tianyu Yang +1
We study the problem of providing channel state information (CSI) at the transmitter in multi-user massive MIMO systems operating in frequency division duplexing (FDD). The wideban…
Channel State Acquisition in FDD Massive MIMO: Rate-Distortion Bound and Effectiveness of "Analog" Feedback
Mahdi Barzegar Khalilsarai, Yi Song, Tianyu Yang +1
We consider the problem of estimating channel fading coefficients (modeled as a correlated Gaussian vector) via Downlink (DL) training and Uplink (UL) feedback in wideband FDD mass…
WiFi-Based Channel Impulse Response Estimation and Localization via Multi-Band Splicing
Mahdi Barzegar Khalilsarai, Benedikt Gross, Stelios Stefanatos +2
Using commodity WiFi data for applications such as indoor localization, object identification and tracking and channel sounding has recently gained considerable attention. We study…
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…
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…
Wideband Massive MIMO Channel Estimation via Sequential Atomic Norm Minimization
Stelios Stefanatos, Mahdi Barzegar Khalilsarai, Gerhard Wunder
The recently introduced atomic norm minimization (ANM) framework for parameter estimation is a promising candidate towards low overhead channel estimation in wireless communication…
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…
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…
Compressive Estimation of a Stochastic Process with Unknown Autocorrelation Function
Mahdi Barzegar Khalilsarai, Saeid Haghighatshoar, Giuseppe Caire +1
In this paper, we study the prediction of a circularly symmetric zero-mean stationary Gaussian process from a window of observations consisting of finitely many samples. This is a…
Matrix Coherency Graph: A Tool for Improving Sparse Coding Performance
Mohsen Joneidi, Mahdi Barzegar Khalilsarai, Alireza Zaeemzadeh +1
Exact recovery of a sparse solution for an underdetermined system of linear equations implies full search among all possible subsets of the dictionary, which is computationally int…