Model-free ultra reliable low latency communication (URLLC): A deep reinforcement learning framework
ATZ Kasgari, W Saad - ICC 2019-2019 IEEE International …, 2019 - ieeexplore.ieee.org
In this paper, a novel deep reinforcement learning (deep-RL) framework is proposed to
provide model-free ultra reliable low latency communication (URLLC) in the downlink of an …
provide model-free ultra reliable low latency communication (URLLC) in the downlink of an …
Reinforcement learning for real-time optimization in NB-IoT networks
NarrowBand Internet of Things (NB-IoT) is an emerging cellular-based technology that offers
a range of flexible configurations for massive IoT radio access from groups of devices with …
a range of flexible configurations for massive IoT radio access from groups of devices with …
A deep reinforcement learning scheme for sum rate and fairness maximization among d2d pairs underlaying cellular network with noma
Device-to-device (D2D) communication is an emerging technology in 5G and the upcoming
6G networks due to its properties to enhanced sum rate. Despite these advantages, co …
6G networks due to its properties to enhanced sum rate. Despite these advantages, co …
Comprehensive review on ML-based RIS-enhanced IoT systems: basics, research progress and future challenges
Sixth generation (6G) internet of things (IoT) networks will modernize the applications and
satisfy user demands through implementing smart and automated systems. Intelligence …
satisfy user demands through implementing smart and automated systems. Intelligence …
Deep learning for radio resource allocation with diverse quality-of-service requirements in 5G
To accommodate diverse Quality-of-Service (QoS) requirements in 5th generation cellular
networks, base stations need real-time optimization of radio resources in time-varying …
networks, base stations need real-time optimization of radio resources in time-varying …
Resource scheduling based on deep reinforcement learning in UAV assisted emergency communication networks
Unmanned aerial vehicle (UAV) assisted emergency communication is an important
technique for future B5G/6G scenario. The UAV is usually considered as a mobile relay to …
technique for future B5G/6G scenario. The UAV is usually considered as a mobile relay to …
Machine learning empowered resource allocation in IRS aided MISO-NOMA networks
A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output
(MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station …
(MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station …
On-policy vs. off-policy deep reinforcement learning for resource allocation in open radio access network
N Hammami, KK Nguyen - 2022 IEEE Wireless …, 2022 - ieeexplore.ieee.org
Recently, Deep Reinforcement Learning (DRL) has increasingly been used to solve
complex problems in mobile networks. There are two main types of DRL models: off-policy …
complex problems in mobile networks. There are two main types of DRL models: off-policy …
The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …