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When a user moves towards a cell edge (away from its local BS), the SNR it receives decreases due to path loss while the interference it encounters from other BSs increases.
At low signal-to-noise ratio (SNR) the fuzzy inference-based beamformer relies on the fuzzy description of the DOA estimation, and at high SNR it places more emphasis on the estimated DOA. Interference rejection is well achieved for Interference to noise ratios (INRs) over the SNR.
For a high SNR, it is reasonable to assume v i ≫ sin2ϕ.
For small values of SNR, it has been shown that the correction term takes the form of (3).
While digital video may suffer from lower SNR, it also provides 3D data that often has significant temporal redundancy [2].
We can observe that asymptotically varying term is dominating at low SNR, but at high SNR it vanishes.
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For the targets with unknown SNRs, it is assumed that the range of the SNRs of the targets is [SNR l, SNR h ], corresponding to [I k,l),I k,h)].
However, at small transmit SNRs it has a slightly worse performance.
It provides a useful indication of the code performance at SNRs below the cut-off rate, and, at high SNRs, it joins with the union bound to predict the error floor.
It is evident from the plots that with the increase in noise, the overlap between the genuine and the impostor score distributions also increases, and under low SNRs, it shows the worst case performance.
When the SUs have different detection SNRs, it is not efficient to use the K-out-of-N fusion rule since it ignores the difference between decisions from a SU with high detection SNR and a SU with low detection SNR.
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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