Mobile Communications, Computing, and Caching Resources Allocation for Diverse Services via Multi-Objective Proximal Policy Optimization

Chen, Zhiyong; Yin, Benshun; Zhu, Haoyu; Li, Yingjiao; Tao, Meixia; Zhang, Wenjun

doi:10.1109/tcomm.2022.3173005

Cited by 10 publications

(2 citation statements)

References 44 publications

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“…In 5G networks, PPO is applied to optimize the communications, computing, and caching (3C) resources of BSs and mobile devices to provide differentiated QoS 105 . The proposed multi‐objective PPO (MO‐PPO) algorithm outperforms traditional methods in finding a higher quality set of Pareto optimal solutions and can more appropriately allocate 3C resources to different types of services.…”

Section: Drl‐based Sac In C‐ranmentioning

confidence: 99%

Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey

Khani,

Jamali,

Sohrabi

et al. 2024

Int J Communication

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SummaryThe emergence of 5G networks has increased the demand for network resources, making efficient resource management crucial. Slice admission control (SAC) is a process that governs the creation and allocation of virtualized network environments, known as “network slices,” which can be tailored to meet specific user requirements. However, traditional SAC methods face dynamic and heterogeneous challenges in wireless networks, especially in cloud radio access networks (C‐RANs). To address this issue, machine learning (ML) techniques, particularly deep reinforcement learning (DRL), have been proposed as powerful tools for optimizing SAC. DRL‐based approaches enable SAC systems to learn from previous interactions with the network environment and dynamically adapt to changing network conditions. This review article comprehensively explains the current state‐of‐the‐art DRL‐based SAC, focusing on C‐RANs. The article identifies key challenges and future research directions and highlights the potential benefits of using DRL for SAC, including improved network performance and efficiency. However, deploying these systems in real‐world scenarios presents several challenges and trade‐offs that need to be carefully considered. Further research and development are required to address these challenges and ensure the successful deployment of DRL‐based SAC systems in wireless networks.

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Section: Drl‐based Sac In C‐ranmentioning

confidence: 99%

Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey

Khani,

Jamali,

Sohrabi

et al. 2024

Int J Communication

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“…This ensures that the policy update does not deviate too far from the previous policy 78 . In the paper, 79 the authors propose a framework to allocate resources for base stations and mobile devices, using a multi‐objective PPO algorithm to solve a multi‐objective programming problem. This approach surpasses conventional methods in discovering superior solutions for resource allocation across various service types.…”

Section: Literature Reviewmentioning

confidence: 99%

Resource allocation in 5G cloud‐RAN using deep reinforcement learning algorithms: A review

Khani,

Jamali,

Sohrabi

et al. 2023

Trans Emerging Tel Tech

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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 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.

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Adaptive Mayfly Optimization Based Multi-objective Task Scheduling on Cloud Environment

Nivethithai,

Hariharan

2024

Information Systems Engineering and Management

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Mobile Communications, Computing, and Caching Resources Allocation for Diverse Services via Multi-Objective Proximal Policy Optimization

Cited by 10 publications

References 44 publications

Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey

Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey

Resource allocation in 5G cloud‐RAN using deep reinforcement learning algorithms: A review

Adaptive Mayfly Optimization Based Multi-objective Task Scheduling on Cloud Environment

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