Yes, we described RR as the fairest in terms of resource allocation.
We created the user cluster by generating UE positions of x and y coordinates from a Gaussian distribution with mean 0 and conditional (depending on the degree of clustering required for scenario) standard deviation σ.
Thank you for your comment on round robin. We indeed mean fair in the resource allocation sense.
We create a cluster by generating normal random variables for the x and y coordinates of UEs. The mean is zero; i.e. the cluster is at the center of the cell. We control the degree of clustering by adjusting the standard deviation. Also, we use the rejection method to make sure that the generated coordinates are within the hexagonal cell. (Please also refer to Slide 9.) We wanted to investigate what happens if users are gathered around the macro BS rather than being uniformly distributed throughout the cell.
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