2,540 research outputs found
Single Stage DOA-Frequency Representation of the Array Data with Source Reconstruction Capability
In this paper, a new signal processing framework is proposed, in which the
array time samples are represented in DOA-frequency domain through a single
stage problem. It is shown that concatenated array data is well represented in
a dictionary atoms space, where columns correspond to
pixels in the DOA-frequency image. We present two approaches for the
formation and compare the benefits and disadvantages of them. A
mutual coherence guaranteed manipulation technique is also
proposed. Furthermore, unlike most of the existing methods, the proposed
problem is reversible into the time domain, therefore, source recovery from the
resulted DOA-frequency image is possible. The proposed representation in
DOA-frequency domain can be simply transformed into a group sparse problem, in
the case of non-multitone sources in a given bandwidth. Therefore, it can also
be utilized as an effective wideband DOA estimator. In the simulation part, two
scenarios of multitone sources with unknown frequency and DOA locations and
non-multitone wideband sources with assumed frequency region are examined. In
multitone scenario, sparse solvers yield more accurate DOA-frequency
representation compared to some noncoherent approaches. At the latter scenario,
the proposed method with group sparse solver outperforms some existing wideband
DOA estimators in low SNR regime. In addition, sources' recovery simultaneous
with DOA estimation shows significant improvement compared to the conventional
delay and sum beamformer and without prerequisites required in sophisticated
wideband beamformers
Gridless Quadrature Compressive Sampling with Interpolated Array Technique
Quadrature compressive sampling (QuadCS) is a sub-Nyquist sampling scheme for
acquiring in-phase and quadrature (I/Q) components in radar. In this scheme,
the received intermediate frequency (IF) signals are expressed as a linear
combination of time-delayed and scaled replicas of the transmitted waveforms.
For sparse IF signals on discrete grids of time-delay space, the QuadCS can
efficiently reconstruct the I/Q components from sub-Nyquist samples. In
practice, the signals are characterized by a set of unknown time-delay
parameters in a continuous space. Then conventional sparse signal
reconstruction will deteriorate the QuadCS reconstruction performance. This
paper focuses on the reconstruction of the I/Q components with continuous delay
parameters. A parametric spectrum-matched dictionary is defined, which sparsely
describes the IF signals in the frequency domain by delay parameters and gain
coefficients, and the QuadCS system is reexamined under the new dictionary.
With the inherent structure of the QuadCS system, it is found that the
estimation of delay parameters can be decoupled from that of sparse gain
coefficients, yielding a beamspace direction-of-arrival (DOA) estimation
formulation with a time-varying beamforming matrix. Then an interpolated
beamspace DOA method is developed to perform the DOA estimation. An optimal
interpolated array is established and sufficient conditions to guarantee the
successful estimation of the delay parameters are derived. With the estimated
delays, the gain coefficients can be conveniently determined by solving a
linear least-squares problem. Extensive simulations demonstrate the superior
performance of the proposed algorithm in reconstructing the sparse signals with
continuous delay parameters.Comment: 34 pages, 11 figure
FDD Massive MIMO Channel Estimation with Arbitrary 2D-Array Geometry
This paper addresses the problem of downlink channel estimation in
frequency-division duplexing (FDD) massive multiple-input multiple-output
(MIMO) systems. The existing methods usually exploit hidden sparsity under a
discrete Fourier transform (DFT) basis to estimate the cdownlink channel.
However, there are at least two shortcomings of these DFT-based methods: 1)
they are applicable to uniform linear arrays (ULAs) only, since the DFT basis
requires a special structure of ULAs, and 2) they always suffer from a
performance loss due to the leakage of energy over some DFT bins. To deal with
the above shortcomings, we introduce an off-grid model for downlink channel
sparse representation with arbitrary 2D-array antenna geometry, and propose an
efficient sparse Bayesian learning (SBL) approach for the sparse channel
recovery and off-grid refinement. The main idea of the proposed off-grid method
is to consider the sampled grid points as adjustable parameters. Utilizing an
in-exact block majorization-minimization (MM) algorithm, the grid points are
refined iteratively to minimize the off-grid gap. Finally, we further extend
the solution to uplink-aided channel estimation by exploiting the angular
reciprocity between downlink and uplink channels, which brings enhanced
recovery performance.Comment: 15 pages, 9 figures, IEEE Transactions on Signal Processing, 201
A Unified Approach to Sparse Signal Processing
A unified view of sparse signal processing is presented in tutorial form by
bringing together various fields. For each of these fields, various algorithms
and techniques, which have been developed to leverage sparsity, are described
succinctly. The common benefits of significant reduction in sampling rate and
processing manipulations are revealed.
The key applications of sparse signal processing are sampling, coding,
spectral estimation, array processing, component analysis, and multipath
channel estimation. In terms of reconstruction algorithms, linkages are made
with random sampling, compressed sensing and rate of innovation. The redundancy
introduced by channel coding in finite/real Galois fields is then related to
sampling with similar reconstruction algorithms. The methods of Prony,
Pisarenko, and MUSIC are next discussed for sparse frequency domain
representations. Specifically, the relations of the approach of Prony to an
annihilating filter and Error Locator Polynomials in coding are emphasized; the
Pisarenko and MUSIC methods are further improvements of the Prony method. Such
spectral estimation methods is then related to multi-source location and DOA
estimation in array processing. The notions of sparse array beamforming and
sparse sensor networks are also introduced. Sparsity in unobservable source
signals is also shown to facilitate source separation in SCA; the algorithms
developed in this area are also widely used in compressed sensing. Finally, the
multipath channel estimation problem is shown to have a sparse formulation;
algorithms similar to sampling and coding are used to estimate OFDM channels.Comment: 43 pages, 40 figures, 15 table
Sensor Array Design Through Submodular Optimization
We consider the problem of far-field sensing by means of a sensor array.
Traditional array geometry design techniques are agnostic to prior information
about the far-field scene. However, in many applications such priors are
available and may be utilized to design more efficient array topologies. We
formulate the problem of array geometry design with scene prior as one of
finding a sampling configuration that enables efficient inference, which turns
out to be a combinatorial optimization problem. While generic combinatorial
optimization problems are NP-hard and resist efficient solvers, we show how for
array design problems the theory of submodular optimization may be utilized to
obtain efficient algorithms that are guaranteed to achieve solutions within a
constant approximation factor from the optimum. We leverage the connection
between array design problems and submodular optimization and port several
results of interest. We demonstrate efficient methods for designing arrays with
constraints on the sensing aperture, as well as arrays respecting combinatorial
placement constraints. This novel connection between array design and
submodularity suggests the possibility for utilizing other insights and
techniques from the growing body of literature on submodular optimization in
the field of array design
Compressed Sensing for Wireless Communications : Useful Tips and Tricks
As a paradigm to recover the sparse signal from a small set of linear
measurements, compressed sensing (CS) has stimulated a great deal of interest
in recent years. In order to apply the CS techniques to wireless communication
systems, there are a number of things to know and also several issues to be
considered. However, it is not easy to come up with simple and easy answers to
the issues raised while carrying out research on CS. The main purpose of this
paper is to provide essential knowledge and useful tips that wireless
communication researchers need to know when designing CS-based wireless
systems. First, we present an overview of the CS technique, including basic
setup, sparse recovery algorithm, and performance guarantee. Then, we describe
three distinct subproblems of CS, viz., sparse estimation, support
identification, and sparse detection, with various wireless communication
applications. We also address main issues encountered in the design of CS-based
wireless communication systems. These include potentials and limitations of CS
techniques, useful tips that one should be aware of, subtle points that one
should pay attention to, and some prior knowledge to achieve better
performance. Our hope is that this article will be a useful guide for wireless
communication researchers and even non-experts to grasp the gist of CS
techniques
FDD Massive MIMO via UL/DL Channel Covariance Extrapolation and Active Channel Sparsification
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-loop DL channel probing and
Channel State Information (CSI) feedback is needed. In massive MIMO this incurs
typically a large training overhead. For example, in a typical configuration
with M = 200 BS antennas and fading coherence block of T = 200 symbols, the
resulting rate penalty factor due to the DL training overhead, given by max{0,
1 - M/T}, is close to 0. To reduce this overhead, we build upon the well-known
fact that the Angular Scattering Function (ASF) of the user channels is
invariant over frequency intervals whose size is small with respect to the
carrier frequency (as in current FDD cellular standards). This allows to
estimate the users' DL channel covariance matrix from UL pilots without
additional overhead. Based on this covariance information, we propose a novel
sparsifying precoder in order to maximize the rank of the effective sparsified
channel matrix subject to the condition that each effective user channel has
sparsity not larger than some desired DL pilot dimension T_{dl}, resulting in
the DL training overhead factor max{0, 1 - T_{dl} / T} and CSI feedback cost of
T_{dl} pilot measurements. The optimization of the sparsifying precoder is
formulated as a Mixed Integer Linear Program, that can be efficiently solved.
Extensive simulation results demonstrate the superiority of the proposed
approach with respect to concurrent state-of-the-art schemes based on
compressed sensing or UL/DL dictionary learning.Comment: 30 pages, 7 figures - Further simulation results and comparisons with
the state-of-the-art techniques, compared to the previous versio
A Block Sparsity Based Estimator for mmWave Massive MIMO Channels with Beam Squint
Multiple-input multiple-output (MIMO) millimeter wave (mmWave) communication
is a key technology for next generation wireless networks. One of the
consequences of utilizing a large number of antennas with an increased
bandwidth is that array steering vectors vary among different subcarriers. Due
to this effect, known as beam squint, the conventional channel model is no
longer applicable for mmWave massive MIMO systems. In this paper, we study
channel estimation under the resulting non-standard model. To that aim, we
first analyze the beam squint effect from an array signal processing
perspective, resulting in a model which sheds light on the angle-delay sparsity
of mmWave transmission. We next design a compressive sensing based channel
estimation algorithm which utilizes the shift-invariant block-sparsity of this
channel model. The proposed algorithm jointly computes the off-grid angles, the
off-grid delays, and the complex gains of the multi-path channel. We show that
the newly proposed scheme reflects the mmWave channel more accurately and
results in improved performance compared to traditional approaches. We then
demonstrate how this approach can be applied to recover both the uplink as well
as the downlink channel in frequency division duplex (FDD) systems, by
exploiting the angle-delay reciprocity of mmWave channels
Analog to Digital Cognitive Radio: Sampling, Detection and Hardware
The proliferation of wireless communications has recently created a
bottleneck in terms of spectrum availability. Motivated by the observation that
the root of the spectrum scarcity is not a lack of resources but an inefficient
managing that can be solved, dynamic opportunistic exploitation of spectral
bands has been considered, under the name of Cognitive Radio (CR). This
technology allows secondary users to access currently idle spectral bands by
detecting and tracking the spectrum occupancy. The CR application revisits this
traditional task with specific and severe requirements in terms of spectrum
sensing and detection performance, real-time processing, robustness to noise
and more. Unfortunately, conventional methods do not satisfy these demands for
typical signals, that often have very high Nyquist rates.
Recently, several sampling methods have been proposed that exploit signals' a
priori known structure to sample them below the Nyquist rate. Here, we review
some of these techniques and tie them to the task of spectrum sensing in the
context of CR. We then show how issues related to spectrum sensing can be
tackled in the sub-Nyquist regime. First, to cope with low signal to noise
ratios, we propose to recover second-order statistics from the low rate
samples, rather than the signal itself. In particular, we consider
cyclostationary based detection, and investigate CR networks that perform
collaborative spectrum sensing to overcome channel effects. To enhance the
efficiency of the available spectral bands detection, we present joint spectrum
sensing and direction of arrival estimation methods. Throughout this work, we
highlight the relation between theoretical algorithms and their practical
implementation. We show hardware simulations performed on a prototype we built,
demonstrating the feasibility of sub-Nyquist spectrum sensing in the context of
CR.Comment: Submitted to IEEE Signal Processing Magazin
OSE and Matrix Filtering for SISA
The expected value and covariance function of a product processor for a spatially uncorrelated white guassian process has been derived for known sparse geometries such as nested or coprime with uniform taper. When a non-uniform taper is applied, the expected value of the product processor becomes the convolution of the true spatial power spectral density and the spatial Fourier transform of the difference coarray. A windowed estimate of the true autocorrelation function can be obtained by taking the inverse Fourier transform of the output. By extending that function to an estimate of the sample covariance matrix, it can be used in subspace-based algorithms such as MUSIC or ESPRIT. These results are extended to colored processes and multidimensional arrays.
Subspace-based algorithms make use of the eigenvectors of the sample covariance matrix or the singular vectors of the received array data. The subspaces spanned by the singular vectors or eigenvectors are perturbed away from the ensemble due to additive noise causing performance losses. This can be improved by using the optimal signal or noise subspace basis estimate instead. A statistically optimal estimate of the unperturbed subspace basis in terms of perturbed signal and orthogonal bases has been derived and is accurate up to the first-order terms in the additive noise matrix. A methodology to find these optimal estimates in sparse arrays that possess a shift-invariant structure is derived here and can be additionally interpolated to match the basis of a fully populated array with equal aperture. The second-order optimal approximation for the unperturbed subspace is derived. The results are applied to a uniform linear array (ULA) processing model for both full and sparse geometries. In the case of a fully sampled array, the first-order terms carry the bulk of information and an extension to second-order does little to reduce estimation error. However, for a sparse array, the performance is improved more clearly.
The variance on DOA estimation using an optimal subspace basis estimate on shift-invariant arrays reaches the Cramer-Rao lower bound. However, unlike other sparse geometries such as nested or coprime arrays, SISAs are not able to estimate more sources than the number of sensors. Presented is the addition of spatial matrix filtering to reduce the rank of the input passed into the estimation algorithm. Matrix filters allow spatial frequencies within its pass band to remain nearly distortionless while simultaneously attenuating ambient noise in the stop band. The reduction in rank does not affect the arrays resolution capability or dimension of the coarray. Therefore we can enable a SISA to estimate more sources than the number of sensors. Derived here is a more generalized optimal subspace estimation algorithm designed to handle the correlation introduced by the matrix filtering pre-processor. By doing so, the SISA achieves unprecedented estimation performance for SNR regions typically considered unusable
- …