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Given a time interval [ts,te], a CKNN query is to retrieve the K-nearest neighbors (KNNs) of a moving user at each time instant within [ts,te].
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This figure shows the packet loss experienced by a user moving at 5 km/h when GPR prediction or CQI averaging are used Fig. 5 Packet loss for user moving at 10 km/h over time sampling intervals.
The results for prediction distances d λ are scalable and could be interpreted as predictions for time horizons of d λ ·λ c /v at a carrier wavelength of λ c and a user moving at velocity v.
Fig. 4 Packet loss for user moving at 5 km/h over time sampling intervals.
Figure 3 shows the normalised goodput of a user moving at 10 km/h when the full feedback, subband-level, best-M and wideband schemes are employed.
This figure shows the packet loss experienced by a user moving at 60 km/h when GPR prediction or CQI averaging are used.
Figure 7 shows an example of the estimated and real CQI values for a user moving at 10 km/h with a prediction window of 10 ms. There is good accordance between the predicted CQIs and the real values.
The figure shows the actual measured and the predicted CQI values for a user moving at 10 km/h. Figure 8 shows the root mean square error (RMSE) of the GPR predictions for different training datasets.
The averaged cell-edge users' throughputs vs. the geometric mean of users' throughputs are plotted for a fast-fading scenario with users moving at 3 km/h.
Use caution when moving the user to another environment.
What we had learnt was that the brand had become so good at optimizing their free users through advertising programs that moving those users over to paid subscribers was not a priority.
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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