Learn to schedule (LEASCH): A deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer
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 …
successful since these tools have been kept in check and updated regularly to fit new …
Double deep Q-network for power allocation in cloud radio access network
Cloud radio access network (CRAN) facilitates resource allocation (RA) by isolating remote
radio heads (RRHs) from baseband units (BBUs). Traditional RA algorithms save energy by …
radio heads (RRHs) from baseband units (BBUs). Traditional RA algorithms save energy by …
Power consumption optimization using gradient boosting aided deep Q-network in C-RANs
Cloud Radio Access Networks (C-RANs) have the potential to enable growing data traffic in
5G networks. However, with the complex states, resource allocation in C-RANs is time …
5G networks. However, with the complex states, resource allocation in C-RANs is time …
AI empowered resource management for future wireless networks
Resource management plays a pivotal role in wireless networks, which, unfortunately, leads
to challenging NP-hard problems. Artificial Intelligence (AI), especially deep learning …
to challenging NP-hard problems. Artificial Intelligence (AI), especially deep learning …
ML-based radio resource management in 5G and beyond networks: A survey
In this survey, a comprehensive study is provided, regarding the use of machine learning
(ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) …
(ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) …
Multi-agent deep reinforcement learning for end—edge orchestrated resource allocation in industrial wireless networks
Edge artificial intelligence will empower the ever simple industrial wireless networks (IWNs)
supporting complex and dynamic tasks by collaboratively exploiting the computation and …
supporting complex and dynamic tasks by collaboratively exploiting the computation and …
Age of information aware radio resource management in vehicular networks: A proactive deep reinforcement learning perspective
In this paper, we investigate the problem of age of information (AoI)-aware radio resource
management for expected long-term performance optimization in a Manhattan grid vehicle …
management for expected long-term performance optimization in a Manhattan grid vehicle …
Deep reinforcement learning paradigm for dense wireless networks in smart cities
Wireless local area networks (WLANs) are widely deployed for Internet-centric data
applications. Due to their extensive norm in our day-to-day wireless-enabled life, WLANs are …
applications. Due to their extensive norm in our day-to-day wireless-enabled life, WLANs are …
Deep reinforcement learning for scheduling in cellular networks
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 …
both industry and academia. A common solution is to replace partial or even all modules in …
Revised reinforcement learning based on anchor graph hashing for autonomous cell activation in cloud-RANs
Cloud radio access networks (C-RANs) have been regarded in recent times as a promising
concept in future 5G technologies where all DSP processors are moved into a central base …
concept in future 5G technologies where all DSP processors are moved into a central base …