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Model-Based Reinforcement Learning With Kernels for Resource Allocation in RAN Slices

Overview of attention for article published in IEEE Transactions on Wireless Communications, August 2022
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

twitter
3 X users

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
17 Mendeley
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Title
Model-Based Reinforcement Learning With Kernels for Resource Allocation in RAN Slices
Published in
IEEE Transactions on Wireless Communications, August 2022
DOI 10.1109/twc.2022.3195570
Authors

Juan J. Alcaraz, Fernando Losilla, Andrea Zanella, Michele Zorzi

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 24%
Student > Master 4 24%
Student > Doctoral Student 1 6%
Lecturer 1 6%
Professor > Associate Professor 1 6%
Other 0 0%
Unknown 6 35%
Readers by discipline Count As %
Computer Science 7 41%
Engineering 3 18%
Social Sciences 1 6%
Unknown 6 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 November 2022.
All research outputs
#14,488,785
of 25,392,582 outputs
Outputs from IEEE Transactions on Wireless Communications
#1,613
of 2,187 outputs
Outputs of similar age
#171,790
of 431,989 outputs
Outputs of similar age from IEEE Transactions on Wireless Communications
#2
of 26 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,187 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 431,989 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.