308 research outputs found
Detection of complex point targets with distributed assets in a MIMO radar system
The report explores the problem of detecting complex point target models in a MIMO radar system. A complex point target is a mathematical and statistical model for a radar target that is not resolved in space, but exhibits varying complex reflectivity across the different bistatic view angles. The complex reflectivity can be modeled as a complex stochastic process whose index set is the set of all the bistatic view angles, and the parameters of the stochastic process follow from an analysis of a target model comprising a number of ideal point scatterers randomly located within some radius of the targets center of mass. The proposed complex point targets may be applicable to statistical inference in multistatic or MIMO radar system. Six different target models are summarized here – three 2-dimensional (Gaussian, Uniform Square, and Uniform Circle) and three 3-dimensional (Gaussian, Uniform Cube, and Uniform Sphere). They are assumed to have different distributions on the location of the point scatterers within the target. We develop data models for the received signals from such targets in the MIMO radar system with distributed assets and partially correlated signals, and consider the resulting detection problem which reduces to the familiar Gauss-Gauss detection problem. We illustrate that the target parameter and transmit signal have an influence on the detector performance through target extent and the SNR respectively. A series of the receiver operator characteristic (ROC) curves are generated to notice the impact on the detector for varying SNR. Kullback–Leibler (KL) divergence is applied to obtain the approximate mean difference between density functions the scatterers assume inside the target models to show the change in the performance of the detector with target extent of the point scatterers
Joint Design of Overlaid Communication Systems and Pulsed Radars
The focus of this paper is on co-existence between a communication system and
a pulsed radar sharing the same bandwidth. Based on the fact that the
interference generated by the radar onto the communication receiver is
intermittent and depends on the density of scattering objects (such as, e.g.,
targets), we first show that the communication system is equivalent to a set of
independent parallel channels, whereby pre-coding on each channel can be
introduced as a new degree of freedom. We introduce a new figure of merit,
named the {\em compound rate}, which is a convex combination of rates with and
without interference, to be optimized under constraints concerning the
signal-to-interference-plus-noise ratio (including {\em signal-dependent}
interference due to clutter) experienced by the radar and obviously the powers
emitted by the two systems: the degrees of freedom are the radar waveform and
the afore-mentioned encoding matrix for the communication symbols. We provide
closed-form solutions for the optimum transmit policies for both systems under
two basic models for the scattering produced by the radar onto the
communication receiver, and account for possible correlation of the
signal-independent fraction of the interference impinging on the radar. We also
discuss the region of the achievable communication rates with and without
interference. A thorough performance assessment shows the potentials and the
limitations of the proposed co-existing architecture
MIMO Interference Alignment Over Correlated Channels with Imperfect CSI
Interference alignment (IA), given uncorrelated channel components and
perfect channel state information, obtains the maximum degrees of freedom in an
interference channel. Little is known, however, about how the sum rate of IA
behaves at finite transmit power, with imperfect channel state information, or
antenna correlation. This paper provides an approximate closed-form
signal-to-interference-plus-noise-ratio (SINR) expression for IA over
multiple-input-multiple-output (MIMO) channels with imperfect channel state
information and transmit antenna correlation. Assuming linear processing at the
transmitters and zero-forcing receivers, random matrix theory tools are
utilized to derive an approximation for the post-processing SINR distribution
of each stream for each user. Perfect channel knowledge and i.i.d. channel
coefficients constitute special cases. This SINR distribution not only allows
easy calculation of useful performance metrics like sum rate and symbol error
rate, but also permits a realistic comparison of IA with other transmission
techniques. More specifically, IA is compared with spatial multiplexing and
beamforming and it is shown that IA may not be optimal for some performance
criteria.Comment: 21 pages, 7 figures, submitted to IEEE Transactions on Signal
Processin
Merging Belief Propagation and the Mean Field Approximation: A Free Energy Approach
We present a joint message passing approach that combines belief propagation
and the mean field approximation. Our analysis is based on the region-based
free energy approximation method proposed by Yedidia et al. We show that the
message passing fixed-point equations obtained with this combination correspond
to stationary points of a constrained region-based free energy approximation.
Moreover, we present a convergent implementation of these message passing
fixedpoint equations provided that the underlying factor graph fulfills certain
technical conditions. In addition, we show how to include hard constraints in
the part of the factor graph corresponding to belief propagation. Finally, we
demonstrate an application of our method to iterative channel estimation and
decoding in an orthogonal frequency division multiplexing (OFDM) system
Space-Time Signal Design for Multilevel Polar Coding in Slow Fading Broadcast Channels
Slow fading broadcast channels can model a wide range of applications in
wireless networks. Due to delay requirements and the unavailability of the
channel state information at the transmitter (CSIT), these channels for many
applications are non-ergodic. The appropriate measure for designing signals in
non-ergodic channels is the outage probability. In this paper, we provide a
method to optimize STBCs based on the outage probability at moderate SNRs.
Multilevel polar coded-modulation is a new class of coded-modulation techniques
that benefits from low complexity decoders and simple rate matching. In this
paper, we derive the outage optimality condition for multistage decoding and
propose a rule for determining component code rates. We also derive an upper
bound on the outage probability of STBCs for designing the
set-partitioning-based labelling. Finally, due to the optimality of the
outage-minimized STBCs for long codes, we introduce a novel method for the
joint optimization of short-to-moderate length polar codes and STBCs
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