Sentence examples for large-scale coefficients from inspiring English sources

A quick inspection of the numbers shows that some large-scale coefficients do not appear on a strong footing, most notably (dot {g}_{4}^{1}).

As this approach needs to share the large-scale coefficients with the network controller for outer precoding computation, it presents a higher computational complexity than non-cooperative approaches.

This choice of β leads to uncertainties on the large-scale coefficients of the secular variation of the order of 0.3−0.5 nT/year, with the largest values attained for zonal coefficients.

The formal uncertainties contained in (mathbf {C}_{mathbf {y}^{o}}^{text {sv}}) are unrealistically small, with a root-mean-squared (rms) value of order 0.05 nT/year for the large-scale coefficients.

The task of gaining knowledge of those coefficients may be unjustifiable in practice due to the excessive, e.g., in case there are L cells serving K users in each one of them, each BS needs to acquire (L−1)K inter-cell large-scale coefficients.

We can write (g_{ilkm} = sqrt {beta _{ilk}} h_{ilkm}) where (sqrt {beta _{ilk}}) is the large-scale coefficient encompassing both path loss and log-normal shadowing.

We assume the same large-scale coefficient value for all BS co-located antennas, and h ilkm is the small-scale coefficient with a circularly symmetric complex normal distribution (mathcal {CN}(0,1)).

Mapping very large scale coefficients to only one state is not appropriate due to the difference information lost.

To accommodate both the basic and superposed information streams into a joint source constellation, the scaling coefficients of basic information streams (left (L_{n}^{b}right)) must be pre-scaled by a factor (phantom {dot {i}!}2^{N_{s}}) to ensure that they lie above the largest scaling coefficient of the superposed part.

The authors also propose a method to estimate the large-scale fading coefficients.

The large-scale fading coefficients are the same as the practical setup for Fig. 5.