Khani, MohsenJamali, ShahramSohrabi, Mohammad KarimSadr, Mohammad MohsenGhaffari, Ali2024-05-192024-05-1920242161-3915https://doi.org10.1002/ett.4929https://hdl.handle.net/20.500.12713/5702This 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 potential of DRL for learning complex decision-making policies without human intervention. The paper first introduces the C-RAN architecture and resource allocation concepts, followed by an overview of DRL algorithms applied to C-RAN. It discusses the challenges and potential solutions in applying DRL to C-RAN resource allocation, including scalability, convergence, and fairness. The review concludes by highlighting open research directions for future investigation. By providing insights into the state-of-the-art techniques for resource allocation in 5G C-RAN using DRL, this paper emphasizes their potential impact on advancing 5G network technology.eninfo:eu-repo/semantics/closedAccessJoint OptimizationC-RanComputational ResourcesRadioNetworksMachineAccessNomaFrameworkSecurityReview Article351WOS:0011351112000012-s2.0-85180894313N/A10.1002/ett.4929Q2