150 research outputs found
Dual-Blind Deconvolution for Overlaid Radar-Communications Systems
The increasingly crowded spectrum has spurred the design of joint
radar-communications systems that share hardware resources and efficiently use
the radio frequency spectrum. We study a general spectral coexistence scenario,
wherein the channels and transmit signals of both radar and communications
systems are unknown at the receiver. In this dual-blind deconvolution (DBD)
problem, a common receiver admits a multi-carrier wireless communications
signal that is overlaid with the radar signal reflected off multiple targets.
The communications and radar channels are represented by continuous-valued
range-time and Doppler velocities of multiple transmission paths and multiple
targets. We exploit the sparsity of both channels to solve the highly ill-posed
DBD problem by casting it into a sum of multivariate atomic norms (SoMAN)
minimization. We devise a semidefinite program to estimate the unknown target
and communications parameters using the theories of positive-hyperoctant
trigonometric polynomials (PhTP). Our theoretical analyses show that the
minimum number of samples required for near-perfect recovery is dependent on
the logarithm of the maximum of number of radar targets and communications
paths rather than their sum. We show that our SoMAN method and PhTP
formulations are also applicable to more general scenarios such as
unsynchronized transmission, the presence of noise, and multiple emitters.
Numerical experiments demonstrate great performance enhancements during
parameter recovery under different scenarios.Comment: 26 pages, 13 figures, 1 tabl
Adaptive Interference Removal for Un-coordinated Radar/Communication Co-existence
Most existing approaches to co-existing communication/radar systems assume
that the radar and communication systems are coordinated, i.e., they share
information, such as relative position, transmitted waveforms and channel
state. In this paper, we consider an un-coordinated scenario where a
communication receiver is to operate in the presence of a number of radars, of
which only a sub-set may be active, which poses the problem of estimating the
active waveforms and the relevant parameters thereof, so as to cancel them
prior to demodulation. Two algorithms are proposed for such a joint waveform
estimation/data demodulation problem, both exploiting sparsity of a proper
representation of the interference and of the vector containing the errors of
the data block, so as to implement an iterative joint interference removal/data
demodulation process. The former algorithm is based on classical on-grid
compressed sensing (CS), while the latter forces an atomic norm (AN)
constraint: in both cases the radar parameters and the communication
demodulation errors can be estimated by solving a convex problem. We also
propose a way to improve the efficiency of the AN-based algorithm. The
performance of these algorithms are demonstrated through extensive simulations,
taking into account a variety of conditions concerning both the interferers and
the respective channel states
Atomic Norm decomposition for sparse model reconstruction applied to positioning and wireless communications
This thesis explores the recovery of sparse signals, arising in the wireless communication and radar system fields, via atomic norm decomposition. Particularly, we
focus on compressed sensing gridless methodologies, which avoid the always existing
error due to the discretization of a continuous space in on-grid methods. We define
the sparse signal by means of a linear combination of so called atoms defined in a
continuous parametrical atom set with infinite cardinality. Those atoms are fully
characterized by a multi-dimensional parameter containing very relevant information
about the application scenario itself. Also, the number of composite atoms is
much lower than the dimension of the problem, which yields sparsity. We address
a gridless optimization solution enforcing sparsity via atomic norm minimization to
extract the parameters that characterize the atom from an observed measurement
of the model, which enables model recovery. We also study a machine learning approach to estimate the number of composite atoms that construct the model, given
that in certain scenarios this number is unknown.
The applications studied in the thesis lay on the field of wireless communications,
particularly on MIMO mmWave channels, which due to their natural properties can
be modeled as sparse. We apply the proposed methods to positioning in automotive
pulse radar working in the mmWave range, where we extract relevant information
such as angle of arrival (AoA), distance and velocity from the received echoes of
objects or targets. Next we study the design of a hybrid precoder for mmWave
channels which allows the reduction of hardware cost in the system by minimizing
as much as possible the number of required RF chains. Last, we explore full channel
estimation by finding the angular parameters that model the channel. For all
the applications we provide a numerical analysis where we compare our proposed
method with state-of-the-art techniques, showing that our proposal outperforms the
alternative methods.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Juan José Murillo Fuentes.- Secretario: Pablo MartÃnez Olmos.- Vocal: David Luengo GarcÃ
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