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20172024
most citedLearn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer

91 citations · 106 across the 3 of their papers we have counts for

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cs.NI2017★ 3 cited

D3.2: SPEED-5G enhanced functional and system architecture, scenarios and performance evaluation metrics

Shahid Mumtaz, Kazi Saidul, Huq Jonathan Rodriguez +36

This deliverable contains a detailed description of the use cases considered in SPEED-5G, which will be used as a basis for demonstration in project. These use cases are Dynamic Ch…