# Saeid HaghighatshoarTechnische Universität Berlin | TUB · Department of Telecommunication Systems

Saeid Haghighatshoar

PhD

## About

69

Publications

6,133

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1,263

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Introduction

Additional affiliations

January 2015 - June 2016

January 2015 - June 2016

September 2010 - December 2014

## Publications

Publications (69)

In massive MIMO systems, the knowledge of channel covariance matrix is crucial for MMSE channel estimation in the uplink and plays an important role in several downlink multiuser beamforming schemes. Due to the large number of base station antennas in massive MIMO, accurate covariance estimation is challenging especially in the case where the numbe...

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. Dual-polarized arrays are commonly employed due to the favorable property that, in principle, they can double the number of channel spatial degrees of freedom with a less-tha...

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 with a large number
$M$
of antennas and a large number of wireless single-antenna devices (users). We consider a block fading channel model where the
$M$
-dimensional...

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. Employing dual-polarized elements in a massive MIMO array has been common practice recently and can, in principle, double the number of spatial degrees of freedom with a less...

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 matrices given a collection of their associated i.i.d vector realizations. This problem has a direct application in multi-user MIMO communications, where the base statio...

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 does not hold and explicit downlink (DL) probing and uplink (UL) feedback are needed in order to achieve spatial multiplexing gain. To support massive MIMO, the overhead i...

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 large number $M$ of antennas and a large number of wireless single-antenna devices (users). We consider a block fading channel model where the $M$-dimensional channel vec...

Obtaining channel covariance knowledge is of great importance in various Multiple-Input Multiple-Output MIMO communication applications, including channel estimation and covariance-based user grouping. In a massive MIMO system, covariance estimation proves to be challenging due to the large number of antennas (M>>1) employed in the base station and...

Obtaining channel covariance knowledge is of great importance in various Multiple-Input Multiple-Output MIMO communication applications, including channel estimation and covariance-based user grouping. In a massive MIMO system, covariance estimation proves to be challenging due to the large number of antennas (M 1) employed in the base station and...

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 Station) occurs through scattering clusters located in the far field of the receiving antenna array. The Angular Spread Function (ASF) of the corresponding M-dim channel...

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 with a large number $M$ of antennas and a large number of wireless single-antenna devices (users). We consider a block fading channel model where the $M$-dimensional channel...

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 reliable methods for beamforming at both the User Equipment (UE) and the Base Station (BS) sides, in order to achieve a sufficiently large Signal-to-Noise Ratio (SNR) afte...

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 base station) and no channel state information is given to the receiver (fully non-coherent detection). Our coding approach borrows the concatenated coding idea from Ama...

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 Uplink (UL) and Downlink (DL), in FDD systems channel reciprocity does not hold. Hence, in order to provide DL channel state information to the Base Station (BS), closedloo...

Efficient and reliable estimation in many signal processing problems encountered in applications requires adopting sparsity prior in a suitable basis on the signals and using techniques from compressed sensing (CS). In this paper, we study a CS problem known as Multiple Measurement Vectors (MMV) problem, which arises in joint estimation of multiple...

Massive MIMO is a variant of multiuser MIMO in which the number of antennas at the base station (BS) M is very large and typically much larger than the number of served users (data streams) K. Recent research has widely investigated the system-level advantages of massive MIMO and, in particular, the beneficial effect of increasing the number of ant...

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 reliable methods for beamforming at both the User Equipment (UE) and the Base Station (BS) sides, in order to achieve a sufficiently large Signal-to-Noise Ratio (SNR) afte...

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 as a maximum-a-posterior estimator whose postulated prior is proportional to the regularization term. In this paper, we study distributed sensing networks in which a set...

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) approach is pursued. Unfortunately, due to the Kronecker-type sensing (measurement) matrix corresponding to this setup, application of standard CS algorithms and analysis metho...

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) approach is pursued. Unfortunately, due to the Kronecker-type sensing (measurement) matrix corresponding to this setup, application of standard CS algorithms and analysis metho...

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 Uplink (UL) and Downlink (DL), in FDD systems channel reciprocity does not hold. Hence, in order to provide DL channel state information to the Base Station (BS), closed-loo...

In this paper, we study the problem of activity detection (AD) in a massive MIMO setup, where the Base Station (BS) has $M \gg 1$ antennas. We consider a flat fading channel model where the $M$-dim channel vector of each user remains almost constant over a coherence block (CB) containing $D_c$ signal dimensions. We study a setting in which the numb...

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 system, the communication between each BS with $M \gg 1$ antennas and each single-antenna user occurs through a collection of scatterers in the environment, where the channel...

In this paper, we study the recovery of a signal from a set of noisy linear projections (measurements), when such projections are unlabeled, that is, the correspondence between the measurements and the set of projection vectors (i.e., the rows of the measurement matrix) is not known a priori. We consider a special case of unlabeled sensing referred...

Massive MIMO is a variant of multiuser MIMO in which the number of antennas at the base station (BS) $M$ is very large and typically much larger than the number of served users (data streams) $K$. Recent research has illustrated the system-level advantages of such a system and in particular the beneficial effect of increasing the number of antennas...

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 the knowledge of the channel. We refer to the problem of finding a narrow beam pair at the transmitter and at the receiver, yielding high Signal to Noise Ratio (SNR) as Be...

The estimation of direction of arrivals with help of $TV$-minimization is studied. Contrary to prior work in this direction, which has only considered certain antenna placement designs, we consider general antenna geometries. Applying the soft-recovery framework, we are able to derive a theoretic guarantee for a certain direction of arrival to be a...

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) operation. Our main objective is to reduce the large overhead incurred by Downlink (DL) common training and Uplink (UL) feedback needed to obtain channel state information (C...

Millimeter-Wave (mm-Wave) band provides orders of magnitude higher bandwidth compared with the traditional sub-6 GHz band. Communication at mm-Waves is, however, quite challenging due to the severe path loss. To cope with this problem, a directional beamforming both at the Base Station (BS) side and the user side is necessary to find a strong path...

In this paper, we show that the Hadamard matrix acts as an extractor over the reals of the Rényi Information Dimension (RID), in an analogous way to how it acts as an extractor of the discrete entropy over finite fields. More precisely, we prove that the RID of an i.i.d. sequence of mixture random variables polarizes to the extremal values of 0 and...

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 larger than the number of users simultaneously served. Massive MIMO can be implemented with Time Division Duplexing (TDD) or Frequency Division Duplexing (FDD) operation. FD...

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 prevalent problem in a wide range of applications in communication theory and signal processing. Due to stationarity, when the autocorrelation function or equivalently th...

We consider a massive MIMO system, based on Time Division Duplexing (TDD) and channel reciprocity, where the base stations learn the channel vectors of their users via the pilots transmitted by the users in the uplink time-frequency slots. It has been observed that, in the limit of very large number of antennas, the only factor limiting the achieva...

We consider a multi-cell massive MIMO system in which every base station is equipped with an array with M ≫ 1 antennas and serves a collection of users inside its cell. We study a scenario in which the base station and the users use OFDM signaling, and the channel state information (CSI) is estimated under time division duplexing (TDD) via orthogon...

Massive MIMO is a variant of multiuser MIMO (Multi-Input Multi-Output) system, where the number of base-station antennasM is very large and generally much larger than the number of spatially multiplexed data streams. Unfortunately, the front-end A/D conversion necessary to drive hundreds of antennas, with a signal bandwidth of 10 to 100 MHz, requir...

Massive MIMO is a variant of multiuser MIMO, in which the number of antennas $M$ at the base-station is very large, and generally much larger than the number of spatially multiplexed data streams to the users. It turns out that by increasing the number of antennas $M$ at the base-station, and as a result increasing the spatial resolution of the arr...

In this paper, we study the capacity and degree-of-freedom (DoF) scaling for the continuous-time amplitude limited AWGN channels in radio frequency (RF) and intensity modulated optical communication (OC) channels. More precisely, we study how the capacity varies in terms of the OFDM block transmission time $T$, bandwidth $W$, amplitude $A$, and the...

In order to cope with the large path-loss exponent of mm-Wave channels, a high beamforming gain is needed. This can be achieved with small hardware complexity and high hardware power efficiency by Hybrid Digital-Analog (HDA) beamforming, where a very large number $M\gg 1$ of antenna array elements requires only a relatively small $m\ll M$ number of...

We study the problem of solving a linear sensing system when the observations
are unlabeled. Specifically we seek a solution to a linear system of equations
y = Ax when the order of the observations in the vector y is unknown. Focusing
on the setting in which A is a random matrix with i.i.d. entries, we show that
if the sensing matrix A admits an o...

Millimeter-wave (mm-Wave) cellular systems are a promising option for a very
high data rate communication because of the large bandwidth available at
mm-Wave frequencies. Due to the large path-loss exponent in the mm-Wave range
of the spectrum, directional beamforming with a large antenna gain is necessary
at the transmitter, the receiver or both f...

In this paper, we propose efficient algorithms for estimating the signal
subspace of mobile users in a wireless communication environment with a
multi-antenna base-station with $M$ antennas. We assume that, for reducing the
RF front-end complexity and overall A/D conversion rate, the JSDM
transmitter/receiver is split into the product of a baseband...

We study the problem of solving a linear sensing system when the observations are unlabeled. Specifically we seek a solution to a linear system of equations y = Ax when the order of the observations in the vector y is unknown. Focusing on the setting in which A is a random matrix with i.i.d. entries, we show that if the sensing matrix A admits an o...

A novel localization approach is proposed in order to find the position of an individual source using recordings of a single microphone in a reverberant enclosure. The multipath propagation is modeled by multiple virtual microphones as images of the actual single microphone and a multipath distance matrix is constructed whose components consist of...

In this paper, we prove a new identity for the least-square solution of an
over-determined set of linear equation $Ax=b$, where $A$ is an $m\times n$
full-rank matrix, $b$ is a column-vector of dimension $m$, and $m$ (the number
of equations) is larger than or equal to $n$ (the dimension of the unknown
vector $x$). Generally, the equations are inco...

We propose a sparse coding approach to address the problem of source-sensor localization and speech reconstruction. This approach relies on designing a dictionary of spatialized signals by projecting the microphone array recordings into the array manifolds characterized for different locations in a reverberant enclosure using the image model. Spars...

In this paper, the `Approximate Message Passing' (AMP) algorithm, initially
developed for compressed sensing of signals under i.i.d. Gaussian measurement
matrices, has been extended to a multi-terminal setting (MAMP algorithm). It
has been shown that similar to its single terminal counterpart, the behavior of
MAMP algorithm is fully characterized b...

Compressed sensing is a new trend in signal processing for efficient sampling and signal acquisition. The idea is that most real-world signals have a sparse representation in an appropriate basis and this can be exploited to capture the sparse signal by taking only a few linear projections. The recovery is possible by running appropriate low-comple...

A new iterative low complexity algorithm has been presented for computing the
Walsh-Hadamard transform (WHT) of an N dimensional signal with a K-sparse WHT,
where N is a power of two and K=O(N^a), scales sub-linearly in N for some 0< a
<1. Assuming a random support model for the nonzero transform domain
components, the algorithm reconstructs the WH...

This paper shows that the R\'enyi information dimension (RID) of an i.i.d.
sequence of mixture random variables polarizes to the extremal values of 0 and
1 (fully discrete and continuous distributions) when transformed by an Hadamard
matrix. This provides a natural counter-part over the reals of the entropy
polarization phenomenon over finite field...

The entropy power inequality (EPI) provides lower bounds on the differential
entropy of the sum of two independent real-valued random variables in terms of
the individual entropies. Versions of the EPI for discrete random variables
have been obtained for special families of distributions with the differential
entropy replaced by the discrete entrop...

This paper investigates the construction of deterministic matrices preserving
the entropy of random vectors with a given probability distribution. In
particular, it is shown that for random vectors having i.i.d. discrete
components, this is achieved by selecting a subset of rows of a Hadamard matrix
such that (i) the selection is deterministic (ii)...