Deep Q-network for 5G NR downlink scheduling

Deep Reinforcement Learning for Downlink Scheduling in 5G and Beyond Networks: A Review

M Seguin, A Omer, M Koosha… - 2023 IEEE 34th …, 2023 - ieeexplore.ieee.org
The coexistence of a wide variety of different applications with diverse Quality of Service
(QoS) and Quality of Experience (QoE) requirements calls for more sophisticated radio …
被引用次数：2 相关文章 所有 2 个版本
[PDF] arxiv.org

Autoencoder-based radio frequency interference mitigation for SMAP passive radiometer

A Owfi, F Afghah - IGARSS 2023-2023 IEEE International …, 2023 - ieeexplore.ieee.org
Passive space-borne radiometers operating in the 1400-1427 MHz protected frequency
band face radio frequency interference (RFI) from terrestrial sources. With the growth of …
被引用次数：2 相关文章 所有 4 个版本
[PDF] arxiv.org

Multi-User Multi-Application Packet Scheduling for Application-Specific QoE Enhancement Based on Knowledge-Embedded DDPG in 6G RAN

Y Fu, X Wang - arXiv preprint arXiv:2405.01007, 2024 - arxiv.org
The rapidly growing diversity of concurrent applications from both different users and same
devices calls for application-specific Quality of Experience (QoE) enhancement of future …
相关文章 所有 2 个版本

Performance Evaluation of 5G Delay-Sensitive Single-Carrier Multi-User Downlink Scheduling

A Omer, F Malandra, J Chakareski… - 2023 IEEE 34th …, 2023 - ieeexplore.ieee.org
The coexistence of a wide variety of different applications with diverse Quality of Service
(QoS) requirements calls for more sophisticated radio resource scheduling (RRS) in 5G …
相关文章 所有 3 个版本
[PDF] gretsi.fr

[PDF][PDF] Deep Q-learning pour l'ordonnancement de paquets sous contraintes strictes de latence et de taille de buffer

S Nérondat, X Leturc, CJ Le Martret, P Ciblat - gretsi.fr
Nous proposons deux ordonnanceurs basés sur du deep reinforcement learning (DRL)
dans le but de réduire la perte de paquets par expiration et par dépassement de capacité du …
相关文章 所有 2 个版本