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

@article{altam2020,
  title = {Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer},
  author = {F. AL-Tam and N. Correia and J. Rodriguez},
  journal = {IEEE access 2020},
  year = {2020},
  month = {Mar},
  doi = {10.1109/access.2020.3000893},
  url = {https://doi.org/10.1109/access.2020.3000893},
}

F. AL-Tam et al. (2020). Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer. IEEE access 2020. https://doi.org/10.1109/access.2020.3000893

F. AL-Tam, et al.. "Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer." IEEE access 2020, 2020, https://doi.org/10.1109/access.2020.3000893.

F. AL-Tam, et al.. "Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer." IEEE access 2020 (2020). https://doi.org/10.1109/access.2020.3000893

F. AL-Tam et al., 2020. Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer. IEEE access 2020. Available at: <https://doi.org/10.1109/access.2020.3000893>.

TY  - JOUR
TI  - Learn to Schedule (LEASCH): A Deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer
AU  - F. AL-Tam
AU  - N. Correia
AU  - J. Rodriguez
PY  - 2020
JO  - IEEE access 2020
DO  - 10.1109/access.2020.3000893
UR  - https://doi.org/10.1109/access.2020.3000893
ER  -