This paper reviews recent research on resource allocation in 5G cloud‐based radio access
networks (C‐RAN) using deep reinforcement learning (DRL) algorithms. It explores the …

The rapid growth of data traffic has pushed the mobile telecommunication industry towards
the adoption of fifth generation (5G) communications. Cloud radio access network (CRAN) …

Cloud radio access network (C-RAN) is a promising architecture to fulfill the ever-increasing
resource demand in telecommunication networks. In C-RAN, a base station is decoupled …

Nextgeneration networks are envisioned to support management to maximize the user s'
dynamic and agile network quality of service (QoS). Cloud radio access network (CRAN) …

Wireless networks and communication of the future will have to manage an ever-increasing
density of mobile users using a wide range of services and apps, as well as a surge in …

Cloud Radio Access Networks (RANs) have become a key enabling technique for the next
generation (5G) wireless communications, which can meet requirements of massively …

The rapid proliferation of fifth-generation (5G) technology has resulted in a wide range of
applications, posing challenges in managing network resources effectively and efficiently …

Cloud radio access networks (CRANs) have become a key enabling technique for the next
generation wireless communications. Resource allocation in CRANs still needs to be further …

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 …

Multi-Agent Deep Reinforcement Learning (MADRL) has been widely utilized in numerous
next-generation wireless network functions, such as resource allocation in both centralized …