1,485 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
Distributed Reconstruction of Nonlinear Networks: An ADMM Approach
In this paper, we present a distributed algorithm for the reconstruction of
large-scale nonlinear networks. In particular, we focus on the identification
from time-series data of the nonlinear functional forms and associated
parameters of large-scale nonlinear networks. Recently, a nonlinear network
reconstruction problem was formulated as a nonconvex optimisation problem based
on the combination of a marginal likelihood maximisation procedure with
sparsity inducing priors. Using a convex-concave procedure (CCCP), an iterative
reweighted lasso algorithm was derived to solve the initial nonconvex
optimisation problem. By exploiting the structure of the objective function of
this reweighted lasso algorithm, a distributed algorithm can be designed. To
this end, we apply the alternating direction method of multipliers (ADMM) to
decompose the original problem into several subproblems. To illustrate the
effectiveness of the proposed methods, we use our approach to identify a
network of interconnected Kuramoto oscillators with different network sizes
(500~100,000 nodes).Comment: To appear in the Preprints of 19th IFAC World Congress 201
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework
As the modern world becomes increasingly digitized and interconnected,
distributed signal processing has proven to be effective in processing its
large volume of data. However, a main challenge limiting the broad use of
distributed signal processing techniques is the issue of privacy in handling
sensitive data. To address this privacy issue, we propose a novel yet general
subspace perturbation method for privacy-preserving distributed optimization,
which allows each node to obtain the desired solution while protecting its
private data. In particular, we show that the dual variables introduced in each
distributed optimizer will not converge in a certain subspace determined by the
graph topology. Additionally, the optimization variable is ensured to converge
to the desired solution, because it is orthogonal to this non-convergent
subspace. We therefore propose to insert noise in the non-convergent subspace
through the dual variable such that the private data are protected, and the
accuracy of the desired solution is completely unaffected. Moreover, the
proposed method is shown to be secure under two widely-used adversary models:
passive and eavesdropping. Furthermore, we consider several distributed
optimizers such as ADMM and PDMM to demonstrate the general applicability of
the proposed method. Finally, we test the performance through a set of
applications. Numerical tests indicate that the proposed method is superior to
existing methods in terms of several parameters like estimated accuracy,
privacy level, communication cost and convergence rate
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