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We have developed a link gain matrix estimation methodology for distributed nodes in wireless networks.
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The paper by J. Lei et al. proposes a link gain Matrix estimation in distributed large-scale wireless networks, with the goal to devise a methodology that measures a fraction of the links and accurately estimates the rest.
Despite of the variation in numerous application-based algorithms, their methodologies can be roughly classified into two categories: VC matrix estimation (VCME) and variance component (scale factor) estimation (VCE).
The methodology can tolerate actuator loss of effectiveness without utilizing the control input matrix estimation, eliminating the need for persistence of excitation.
The fourth section presents the estimation methodology.
Section 4 explains the estimation methodology.
The notion of the extended dissipativity based state estimation methodology is launched to investigate the L2−L∞ state estimation, H∞ state estimation, passivity state estimation, mixed H∞ and passivity state estimation, and (Q,S,R −γ-dissipativity state estimation of SNNs by choosing free weighting matrices.
Small sample covariance matrix estimation.
The proposed estimation methodology is validated experimentally.
Section three presents data, variables and estimation methodology.
This waste estimation methodology is discussed in abridged form below.
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