280 research outputs found
Sparse Sampling for Inverse Problems with Tensors
We consider the problem of designing sparse sampling strategies for
multidomain signals, which can be represented using tensors that admit a known
multilinear decomposition. We leverage the multidomain structure of tensor
signals and propose to acquire samples using a Kronecker-structured sensing
function, thereby circumventing the curse of dimensionality. For designing such
sensing functions, we develop low-complexity greedy algorithms based on
submodular optimization methods to compute near-optimal sampling sets. We
present several numerical examples, ranging from multi-antenna communications
to graph signal processing, to validate the developed theory.Comment: 13 pages, 7 figure
Submodular Optimization for Placement of Intelligent Reflecting Surfaces in Sensing Systems
Intelligent reflecting surfaces (IRS) and their optimal deployment are the
new technological frontier in sensing applications. Recently, IRS have
demonstrated potential in advancing target estimation and detection. While the
optimal phase-shift of IRS for different tasks has been studied extensively in
the literature, the optimal placement of multiple IRS platforms for sensing
applications is less explored. In this paper, we design the placement of IRS
platforms for sensing by maximizing the mutual information. In particular, we
use this criterion to determine an approximately optimal placement of IRS
platforms to illuminate an area where the target has a hypothetical presence.
After demonstrating the submodularity of the mutual information criteria, we
tackle the design problem by means of a constant-factor approximation algorithm
for submodular optimization. Numerical results are presented to validate the
proposed submodular optimization framework for optimal IRS placement with worst
case performance bounded to
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