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

F Al-Tam, N Correia, J Rodriguez - IEEE Access, 2020 - ieeexplore.ieee.org
Network management tools are usually inherited from one generation to another. This was
successful since these tools have been kept in check and updated regularly to fit new …

Deep reinforcement learning for scheduling in cellular networks

J Wang, C Xu, Y Huangfu, R Li, Y Ge… - 2019 11th International …, 2019 - ieeexplore.ieee.org
Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in
both industry and academia. A common solution is to replace partial or even all modules in …

Radio resource scheduling for 5G NR via deep deterministic policy gradient

SC Tseng, ZW Liu, YC Chou… - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
The fifth generation (5G) wireless system plays a crucial role to realize future network
applications with diverse services requirements. The 3rd Generation Partnership Project …

5MART: A 5G SMART scheduling framework for optimizing QoS through reinforcement learning

IS Comșa, R Trestian, GM Muntean… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
The massive growth in mobile data traffic and the heterogeneity and stringency of Quality of
Service (QoS) requirements of various applications have put significant pressure on the …

Knowledge-assisted deep reinforcement learning in 5G scheduler design: From theoretical framework to implementation

Z Gu, C She, W Hardjawana, S Lumb… - IEEE Journal on …, 2021 - ieeexplore.ieee.org
In this paper, we develop a knowledge-assisted deep reinforcement learning (DRL)
algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with …

Delay-aware cellular traffic scheduling with deep reinforcement learning

T Zhang, S Shen, S Mao… - GLOBECOM 2020-2020 …, 2020 - ieeexplore.ieee.org
Radio access network (RAN) in 5G is expected to satisfy the stringent delay requirements of
a variety of applications. The packet scheduler plays an important role by allocating …

Radio resource management in multi-numerology 5G new radio featuring network slicing

K Boutiba, M Bagaa, A Ksentini - ICC 2022-IEEE International …, 2022 - ieeexplore.ieee.org
5G New Radio (NR) introduces several key features to support the new emerging vertical
industry use-cases, mainly:(1) Different numerology that gives more flexibility in managing …

Towards 5G: A reinforcement learning-based scheduling solution for data traffic management

IS Comşa, S Zhang, ME Aydin… - … on Network and …, 2018 - ieeexplore.ieee.org
Dominated by delay-sensitive and massive data applications, radio resource management
in 5G access networks is expected to satisfy very stringent delay and packet loss …

Scheduling algorithms for 5G networks and beyond: Classification and survey

A Mamane, M Fattah, M El Ghazi, M El Bekkali… - IEEe …, 2022 - ieeexplore.ieee.org
Over the years, several research groups have been developing effective and efficient
scheduling algorithms to enhance the quality of service of mobile communication networks …

Qos-driven scheduling in 5g radio access networks-a reinforcement learning approach

IS Comsa, A De-Domenico… - GLOBECOM 2017-2017 …, 2017 - ieeexplore.ieee.org
The expected diversity of services and the variety of use cases in 5G networks will require a
flexible Radio Resource Management able to satisfy the heterogeneous Quality of Service …