376 research outputs found
Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding
In this work, we propose a subspace-based algorithm for DOA estimation which
iteratively reduces the disturbance factors of the estimated data covariance
matrix and incorporates prior knowledge which is gradually obtained on line. An
analysis of the MSE of the reshaped data covariance matrix is carried out along
with comparisons between computational complexities of the proposed and
existing algorithms. Simulations focusing on closely-spaced sources, where they
are uncorrelated and correlated, illustrate the improvements achieved.Comment: 7 figures. arXiv admin note: text overlap with arXiv:1703.1052
Dynamic Topology Adaptation Based on Adaptive Link Selection Algorithms for Distributed Estimation
This paper presents adaptive link selection algorithms for distributed
estimation and considers their application to wireless sensor networks and
smart grids. In particular, exhaustive search--based
least--mean--squares(LMS)/recursive least squares(RLS) link selection
algorithms and sparsity--inspired LMS/RLS link selection algorithms that can
exploit the topology of networks with poor--quality links are considered. The
proposed link selection algorithms are then analyzed in terms of their
stability, steady--state and tracking performance, and computational
complexity. In comparison with existing centralized or distributed estimation
strategies, key features of the proposed algorithms are: 1) more accurate
estimates and faster convergence speed can be obtained; and 2) the network is
equipped with the ability of link selection that can circumvent link failures
and improve the estimation performance. The performance of the proposed
algorithms for distributed estimation is illustrated via simulations in
applications of wireless sensor networks and smart grids.Comment: 14 figure
Study of MMSE-Based Resource Allocation for Clustered Cell-Free Massive MIMO Networks
In this paper, a downlink cell-free massive multiple-input multiple-output
(CF massive MIMO) system and a network clustering is considered. Closed form
sum-rate expressions are derived for CF and the clustered CF (CLCF) networks
where linear precoders included zero forcing (ZF) and minimum mean square error
(MMSE) are implemented. An MMSE-based resource allocation technique with
multiuser scheduling based on an enhanced greedy technique and power allocation
based on the gradient descent (GD) method is proposed in the CLCF network to
improve the system performance. Numerical results show that the proposed
technique is superior to the existing approaches and the computational cost and
the signaling load are essentially reduced in the CLCF network.Comment: 6 pages, 2 figure
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