70 research outputs found
Diffusion LMS for clustered multitask networks
Recent research works on distributed adaptive networks have intensively
studied the case where the nodes estimate a common parameter vector
collaboratively. However, there are many applications that are
multitask-oriented in the sense that there are multiple parameter vectors that
need to be inferred simultaneously. In this paper, we employ diffusion
strategies to develop distributed algorithms that address clustered multitask
problems by minimizing an appropriate mean-square error criterion with
-regularization. Some results on the mean-square stability and
convergence of the algorithm are also provided. Simulations are conducted to
illustrate the theoretical findings.Comment: 5 pages, 6 figures, submitted to ICASSP 201
Approximate maximum likelihood direction of arrival estimation for two closely spaced sources
Abstract—Most high resolution direction of arrival (DoA) estimation algorithms exploit an eigen decomposition of the sample covariance matrix (SCM). However, their performance dramatically degrade in case of correlated sources or low number of snapshots. In contrast, the maximum likelihood (ML) DoA estimator is more robust to these drawbacks but suffers from a too expensive computational cost which can prevent its use in practice. In this paper, we propose an asymptotic simplification of the ML criterion in the case of two closely spaced sources. This approximated ML estimator can be implemented using only 1-D Fourier transforms. We show that this solution is as accurate as the exact ML one and outperforms all high-resolution techniques in case of correlated sources. This solution can also be used in the single snapshot case where very few algorithms are known to be effective
R-dimensional ESPRIT-type algorithms for strictly second-order non-circular sources and their performance analysis
High-resolution parameter estimation algorithms designed to exploit the prior
knowledge about incident signals from strictly second-order (SO) non-circular
(NC) sources allow for a lower estimation error and can resolve twice as many
sources. In this paper, we derive the R-D NC Standard ESPRIT and the R-D NC
Unitary ESPRIT algorithms that provide a significantly better performance
compared to their original versions for arbitrary source signals. They are
applicable to shift-invariant R-D antenna arrays and do not require a
centrosymmetric array structure. Moreover, we present a first-order asymptotic
performance analysis of the proposed algorithms, which is based on the error in
the signal subspace estimate arising from the noise perturbation. The derived
expressions for the resulting parameter estimation error are explicit in the
noise realizations and asymptotic in the effective signal-to-noise ratio (SNR),
i.e., the results become exact for either high SNRs or a large sample size. We
also provide mean squared error (MSE) expressions, where only the assumptions
of a zero mean and finite SO moments of the noise are required, but no
assumptions about its statistics are necessary. As a main result, we
analytically prove that the asymptotic performance of both R-D NC ESPRIT-type
algorithms is identical in the high effective SNR regime. Finally, a case study
shows that no improvement from strictly non-circular sources can be achieved in
the special case of a single source.Comment: accepted at IEEE Transactions on Signal Processing, 15 pages, 6
figure
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA
Network data are commonly collected in a variety of applications,
representing either directly measured or statistically inferred connections
between features of interest. In an increasing number of domains, these
networks are collected over time, such as interactions between users of a
social media platform on different days, or across multiple subjects, such as
in multi-subject studies of brain connectivity. When analyzing multiple large
networks, dimensionality reduction techniques are often used to embed networks
in a more tractable low-dimensional space. To this end, we develop a framework
for principal components analysis (PCA) on collections of networks via a
specialized tensor decomposition we term Semi-Symmetric Tensor PCA or SS-TPCA.
We derive computationally efficient algorithms for computing our proposed
SS-TPCA decomposition and establish statistical efficiency of our approach
under a standard low-rank signal plus noise model. Remarkably, we show that
SS-TPCA achieves the same estimation accuracy as classical matrix PCA, with
error proportional to the square root of the number of vertices in the network
and not the number of edges as might be expected. Our framework inherits many
of the strengths of classical PCA and is suitable for a wide range of
unsupervised learning tasks, including identifying principal networks,
isolating meaningful changepoints or outlying observations, and for
characterizing the "variability network" of the most varying edges. Finally, we
demonstrate the effectiveness of our proposal on simulated data and on an
example from empirical legal studies. The techniques used to establish our main
consistency results are surprisingly straightforward and may find use in a
variety of other network analysis problems
Impact of fast-converging PEVD algorithms on broadband AoA estimation
Polynomial matrix eigenvalue decomposition (PEVD) algorithms have been shown to enable a solution to the broadband angle of arrival (AoA) estimation problem. A parahermitian cross-spectral density (CSD) matrix can be generated from samples gathered by multiple array elements. The application of the PEVD to this CSD matrix leads to a paraunitary matrix which can be used within the spatio-spectral polynomial multiple signal classification (SSP-MUSIC) AoA estimation algorithm. Here, we demonstrate that the recent low-complexity divide-and-conquer sequential matrix diagonalisation (DC-SMD) algorithm, when paired with SSP-MUSIC, is able to provide superior AoA estimation versus traditional PEVD methods for the same algorithm execution time. We also provide results that quantify the performance trade-offs that DC-SMD offers for various algorithm parameters, and show that algorithm convergence speed can be increased at the expense of increased decomposition error and poorer AoA estimation performance
MIMO signal processing in offset-QAM based filter bank multicarrier systems
Next-generation communication systems have to comply with very strict requirements for increased flexibility in heterogeneous environments, high spectral efficiency, and agility of carrier aggregation. This fact motivates research in advanced multicarrier modulation (MCM) schemes, such as filter bank-based multicarrier (FBMC) modulation. This paper focuses on the offset quadrature amplitude modulation (OQAM)-based FBMC variant, known as FBMC/OQAM, which presents outstanding spectral efficiency and confinement in a number of channels and applications. Its special nature, however, generates a number of new signal processing challenges that are not present in other MCM schemes, notably, in orthogonal-frequency-division multiplexing (OFDM). In multiple-input multiple-output (MIMO) architectures, which are expected to play a primary role in future communication systems, these challenges are intensified, creating new interesting research problems and calling for new ideas and methods that are adapted to the particularities of the MIMO-FBMC/OQAM system. The goal of this paper is to focus on these signal processing problems and provide a concise yet comprehensive overview of the recent advances in this area. Open problems and associated directions for future research are also discussed.Peer ReviewedPostprint (author's final draft
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