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Through simulation experiments, we show that these proposals are superior or comparable to the standard SD-traffic-matrix estimator in term of estimation accuracy.
The experimental results show the efficiency of the proposed methodology and tool in term of estimation accuracy, reducing the system power estimation time of the simulation technique.
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In fact, both versions of the new technique outperform by far, in terms of estimation accuracy, the RM estimator with an advantage for version II over version I (the advantage of exploiting the Toeplitz structure is now clearer).
In terms of estimation, conceptually, one can estimate β js in a way analogous to linear reduction, i.e. through the eigen decomposition:, where Σ ϕ= Cov{ ϕ(X)} and Ωϕ is defined similarly as Ω x except we replace X with ϕ(X).
Several computer simulation based experiments are performed to confirm that the proposed methods can reconstruct the original channel coefficient more effectively when compared to the conventional channel estimator in terms of estimation accuracy.
In the finite-dimensional setting, we derive mild sufficient conditions which ensure that a distributed estimator performs better than the local optimal ones in terms of estimation error variance.
We now compare both algorithms in terms of estimation accuracy.
The efficacy of these designs is studied, both in terms of estimation efficiency and discrimination success.
Also, they consider the same problem in terms of estimation or compensation.
Therefore, the proposed TBD algorithm outperforms the benchmark method in terms of estimation accuracy.
Also, they further consider the same problem in terms of estimation or compensation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com