9,860 research outputs found
Low-Complexity OFDM Spectral Precoding
This paper proposes a new large-scale mask-compliant spectral precoder
(LS-MSP) for orthogonal frequency division multiplexing systems. In this paper,
we first consider a previously proposed mask-compliant spectral precoding
scheme that utilizes a generic convex optimization solver which suffers from
high computational complexity, notably in large-scale systems. To mitigate the
complexity of computing the LS-MSP, we propose a divide-and-conquer approach
that breaks the original problem into smaller rank 1 quadratic-constraint
problems and each small problem yields closed-form solution. Based on these
solutions, we develop three specialized first-order low-complexity algorithms,
based on 1) projection on convex sets and 2) the alternating direction method
of multipliers. We also develop an algorithm that capitalizes on the
closed-form solutions for the rank 1 quadratic constraints, which is referred
to as 3) semi-analytical spectral precoding. Numerical results show that the
proposed LS-MSP techniques outperform previously proposed techniques in terms
of the computational burden while complying with the spectrum mask. The results
also indicate that 3) typically needs 3 iterations to achieve similar results
as 1) and 2) at the expense of a slightly increased computational complexity.Comment: Accepted in IEEE International Workshop on Signal Processing Advances
in Wireless Communications (SPAWC), 201
Optimal Linear Precoding Strategies for Wideband Non-Cooperative Systems based on Game Theory-Part II: Algorithms
In this two-part paper, we address the problem of finding the optimal
precoding/multiplexing scheme for a set of non-cooperative links sharing the
same physical resources, e.g., time and bandwidth. We consider two alternative
optimization problems: P.1) the maximization of mutual information on each
link, given constraints on the transmit power and spectral mask; and P.2) the
maximization of the transmission rate on each link, using finite order
constellations, under the same constraints as in P.1, plus a constraint on the
maximum average error probability on each link. Aiming at finding decentralized
strategies, we adopted as optimality criterion the achievement of a Nash
equilibrium and thus we formulated both problems P.1 and P.2 as strategic
noncooperative (matrix-valued) games. In Part I of this two-part paper, after
deriving the optimal structure of the linear transceivers for both games, we
provided a unified set of sufficient conditions that guarantee the uniqueness
of the Nash equilibrium. In this Part II, we focus on the achievement of the
equilibrium and propose alternative distributed iterative algorithms that solve
both games. Specifically, the new proposed algorithms are the following: 1) the
sequential and simultaneous iterative waterfilling based algorithms,
incorporating spectral mask constraints; 2) the sequential and simultaneous
gradient projection based algorithms, establishing an interesting link with
variational inequality problems. Our main contribution is to provide sufficient
conditions for the global convergence of all the proposed algorithms which,
although derived under stronger constraints, incorporating for example spectral
mask constraints, have a broader validity than the convergence conditions known
in the current literature for the sequential iterative waterfilling algorithm.Comment: Paper submitted to IEEE Transactions on Signal Processing, February
22, 2006. Revised March 26, 2007. Accepted June 5, 2007. To appear on IEEE
Transactions on Signal Processing, 200
Learning Wavefront Coding for Extended Depth of Field Imaging
Depth of field is an important factor of imaging systems that highly affects
the quality of the acquired spatial information. Extended depth of field (EDoF)
imaging is a challenging ill-posed problem and has been extensively addressed
in the literature. We propose a computational imaging approach for EDoF, where
we employ wavefront coding via a diffractive optical element (DOE) and we
achieve deblurring through a convolutional neural network. Thanks to the
end-to-end differentiable modeling of optical image formation and computational
post-processing, we jointly optimize the optical design, i.e., DOE, and the
deblurring through standard gradient descent methods. Based on the properties
of the underlying refractive lens and the desired EDoF range, we provide an
analytical expression for the search space of the DOE, which is instrumental in
the convergence of the end-to-end network. We achieve superior EDoF imaging
performance compared to the state of the art, where we demonstrate results with
minimal artifacts in various scenarios, including deep 3D scenes and broadband
imaging
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