1,081 research outputs found
A Multiple Hypothesis Testing Approach to Low-Complexity Subspace Unmixing
Subspace-based signal processing traditionally focuses on problems involving
a few subspaces. Recently, a number of problems in different application areas
have emerged that involve a significantly larger number of subspaces relative
to the ambient dimension. It becomes imperative in such settings to first
identify a smaller set of active subspaces that contribute to the observation
before further processing can be carried out. This problem of identification of
a small set of active subspaces among a huge collection of subspaces from a
single (noisy) observation in the ambient space is termed subspace unmixing.
This paper formally poses the subspace unmixing problem under the parsimonious
subspace-sum (PS3) model, discusses connections of the PS3 model to problems in
wireless communications, hyperspectral imaging, high-dimensional statistics and
compressed sensing, and proposes a low-complexity algorithm, termed marginal
subspace detection (MSD), for subspace unmixing. The MSD algorithm turns the
subspace unmixing problem for the PS3 model into a multiple hypothesis testing
(MHT) problem and its analysis in the paper helps control the family-wise error
rate of this MHT problem at any level under two random
signal generation models. Some other highlights of the analysis of the MSD
algorithm include: (i) it is applicable to an arbitrary collection of subspaces
on the Grassmann manifold; (ii) it relies on properties of the collection of
subspaces that are computable in polynomial time; and () it allows for
linear scaling of the number of active subspaces as a function of the ambient
dimension. Finally, numerical results are presented in the paper to better
understand the performance of the MSD algorithm.Comment: Submitted for journal publication; 33 pages, 14 figure
Union of Low-Rank Subspaces Detector
The problem of signal detection using a flexible and general model is
considered. Due to applicability and flexibility of sparse signal
representation and approximation, it has attracted a lot of attention in many
signal processing areas. In this paper, we propose a new detection method based
on sparse decomposition in a union of subspaces (UoS) model. Our proposed
detector uses a dictionary that can be interpreted as a bank of matched
subspaces. This improves the performance of signal detection, as it is a
generalization for detectors. Low-rank assumption for the desired signals
implies that the representations of these signals in terms of some proper bases
would be sparse. Our proposed detector exploits sparsity in its decision rule.
We demonstrate the high efficiency of our method in the cases of voice activity
detection in speech processing
Dealing with Interference in Distributed Large-scale MIMO Systems: A Statistical Approach
This paper considers the problem of interference control through the use of
second-order statistics in massive MIMO multi-cell networks. We consider both
the cases of co-located massive arrays and large-scale distributed antenna
settings. We are interested in characterizing the low-rankness of users'
channel covariance matrices, as such a property can be exploited towards
improved channel estimation (so-called pilot decontamination) as well as
interference rejection via spatial filtering. In previous work, it was shown
that massive MIMO channel covariance matrices exhibit a useful finite rank
property that can be modeled via the angular spread of multipath at a MIMO
uniform linear array. This paper extends this result to more general settings
including certain non-uniform arrays, and more surprisingly, to two dimensional
distributed large scale arrays. In particular our model exhibits the dependence
of the signal subspace's richness on the scattering radius around the user
terminal, through a closed form expression. The applications of the
low-rankness covariance property to channel estimation's denoising and
low-complexity interference filtering are highlighted.Comment: 12 pages, 11 figures, to appear in IEEE Journal of Selected Topics in
Signal Processin
Explicit receivers for pure-interference bosonic multiple access channels
The pure-interference bosonic multiple access channel has two senders and one
receiver, such that the senders each communicate with multiple temporal modes
of a single spatial mode of light. The channel mixes the input modes from the
two users pairwise on a lossless beamsplitter, and the receiver has access to
one of the two output ports. In prior work, Yen and Shapiro found the capacity
region of this channel if encodings consist of coherent-state preparations.
Here, we demonstrate how to achieve the coherent-state Yen-Shapiro region (for
a range of parameters) using a sequential decoding strategy, and we show that
our strategy outperforms the rate regions achievable using conventional
receivers. Our receiver performs binary-outcome quantum measurements for every
codeword pair in the senders' codebooks. A crucial component of this scheme is
a non-destructive "vacuum-or-not" measurement that projects an n-symbol
modulated codeword onto the n-fold vacuum state or its orthogonal complement,
such that the post-measurement state is either the n-fold vacuum or has the
vacuum removed from the support of the n symbols' joint quantum state. This
receiver requires the additional ability to perform multimode optical
phase-space displacements which are realizable using a beamsplitter and a
laser.Comment: v1: 9 pages, 2 figures, submission to the 2012 International
Symposium on Information Theory and its Applications (ISITA 2012), Honolulu,
Hawaii, USA; v2: minor change
High-Dimensional Matched Subspace Detection When Data are Missing
We consider the problem of deciding whether a highly incomplete signal lies
within a given subspace. This problem, Matched Subspace Detection, is a
classical, well-studied problem when the signal is completely observed. High-
dimensional testing problems in which it may be prohibitive or impossible to
obtain a complete observation motivate this work. The signal is represented as
a vector in R^n, but we only observe m << n of its elements. We show that
reliable detection is possible, under mild incoherence conditions, as long as m
is slightly greater than the dimension of the subspace in question
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