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For these schemes, we first introduce a B-spline hypervolume to approximate an objective function defined in a design space, where the approximation is based on Latin-hypercube sampling points.
The approximation is based on finding small local designs for independent prediction at particular inputs.
The approximation is based on combining Newton's method with the harmonic balance method.
The approximation is based on Shannon's capacity formula adjusted by the two parameters Beff and Aeff[29].
The approximation is based on results from 3D analyses that have been conducted for a variety of cases.
The exact solution involves rather intricate Bessel functions, whereas the approximation is based on easily computable power series.
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The approximations are based on a local interpolation scheme and on an efficient simulation technique.
This is largely due to the fact that the analytical approximation is based on asymptotic analytical solution which ignores the stochastic effects due to the finite number of iterations.
The essence of the supercell approximation is based on this fact.
The proposed approximation is based on the extreme value theory of multivariable functions [24].
The convex approximation is based on the following lower bound: log_{2}left(1+text{SINR}right)geq alphalog_{2}text{SINR} + beta, (10).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com