4 research outputs found
Distributed Low-Rank Adaptive Algorithms Based on Alternating Optimization and Applications
This paper presents a novel distributed low-rank scheme and adaptive
algorithms for distributed estimation over wireless networks. The proposed
distributed scheme is based on a transformation that performs dimensionality
reduction at each agent of the network followed by transmission of a reduced
set of parameters to other agents and reduced-dimension parameter estimation.
Distributed low-rank joint iterative estimation algorithms based on alternating
optimization strategies are developed, which can achieve significantly reduced
communication overhead and improved performance when compared with existing
techniques. A computational complexity analysis of the proposed and existing
low-rank algorithms is presented along with an analysis of the convergence of
the proposed techniques. Simulations illustrate the performance of the proposed
strategies in applications of wireless sensor networks and smart grids.Comment: 12 figures, 13 pages. arXiv admin note: text overlap with
arXiv:1411.112
Compressed Sensing with Probability-based Prior Information
This paper deals with the design of a sensing matrix along with a sparse
recovery algorithm by utilizing the probability-based prior information for
compressed sensing system. With the knowledge of the probability for each atom
of the dictionary being used, a diagonal weighted matrix is obtained and then
the sensing matrix is designed by minimizing a weighted function such that the
Gram of the equivalent dictionary is as close to the Gram of dictionary as
possible. An analytical solution for the corresponding sensing matrix is
derived which leads to low computational complexity. We also exploit this prior
information through the sparse recovery stage and propose a probability-driven
orthogonal matching pursuit algorithm that improves the accuracy of the
recovery. Simulations for synthetic data and application scenarios of
surveillance video are carried out to compare the performance of the proposed
methods with some existing algorithms. The results reveal that the proposed CS
system outperforms existing CS systems.Comment: 13 pages, 9 figure
Study of Distributed Robust Beamforming with Low-Rank and Cross-Correlation Techniques
In this work, we present a novel robust distributed beamforming (RDB)
approach based on low-rank and cross-correlation techniques. The proposed RDB
approach mitigates the effects of channel errors in wireless networks equipped
with relays based on the exploitation of the cross-correlation between the
received data from the relays at the destination and the system output and
low-rank techniques. The relay nodes are equipped with an amplify-and-forward
(AF) protocol and the channel errors are modeled using an additive matrix
perturbation, which results in degradation of the system performance. The
proposed method, denoted low-rank and cross-correlation RDB (LRCC-RDB),
considers a total relay transmit power constraint in the system and the goal of
maximizing the output signal-to-interference-plus-noise ratio (SINR). We carry
out a performance analysis of the proposed LRCC-RDB technique along with a
computational complexity study. The proposed LRCC-RDB does not require any
costly online optimization procedure and simulations show an excellent
performance as compared to previously reported algorithms.Comment: 14 pages, 9 figures. arXiv admin note: text overlap with
arXiv:1712.0111
Study of Diffusion Normalized Least Mean M-estimate Algorithms
This work proposes diffusion normalized least mean M-estimate algorithm based
on the modified Huber function, which can equip distributed networks with
robust learning capability in the presence of impulsive interference. In order
to exploit the system's underlying sparsity to further improve the learning
performance, a sparse-aware variant is also developed by incorporating the
-norm of the estimates into the update process. We then analyze the
transient, steady-state and stability behaviors of the algorithms in a unified
framework. In particular, we present an analytical method that is simpler than
conventional approaches to deal with the score function since it removes the
requirements of integrals and Price's theorem. Simulations in various impulsive
noise scenarios show that the proposed algorithms are superior to some existing
diffusion algorithms and the theoretical results are verifiable.Comment: 14 pages, 13 figure