Equilibrated and Fast Resources Allocation for Massive and Diversified MTC Services Using Multiagent Deep Reinforcement Learning

Tang, Lun; Du, Yucong; Chen, Qianbin; Liu, Qinghai; Li, Jinyu; Li, Shirui

doi:10.1109/jiot.2022.3204359

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…The suggested method outperforms other techniques, and the TD3‐based algorithm reduces the execution time compared to the heuristic algorithm. In Reference [92], the researchers introduce a well‐balanced and efficient approach for RA in a wide range of MTC (Massive Machine Type Communications) services. They employ a multi‐agent DRL (MDRL) technique to address the scheduling of E2E virtual network functions (VNF), as well as the allocation of resources across core network nodes, links, and access network subcarriers, employing various strategies.…”

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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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

View full text Add to dashboard Cite

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Digital-Twin-Assisted VNF Migration Through Resource Prediction in SDN/NVF-Enabled IoT Networks

Tang,

Hou,

Wen

et al. 2024

IEEE Internet Things J.

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A Learning-Based Energy-Efficient Device Grouping Mechanism for Massive Machine-Type Communication in the Context of Beyond 5G Networks

Boisguene,

Althamary,

Huang

2024

JSAN

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With the increasing demand for high data rates, low delay, and extended battery life, managing massive machine-type communication (mMTC) in the beyond 5G (B5G) context is challenging. MMTC devices, which play a role in developing the Internet of Things (IoT) and smart cities, need to transmit short amounts of data periodically within a specific time frame. Although blockchain technology is utilized for secure data storage and transfer while digital twin technology provides real-time monitoring and management of the devices, issues such as constrained time delays and network congestion persist. Without a proper data transmission strategy, most devices would fail to transmit in time, thus defying their relevance and purpose. This work investigates the problem of massive random access channel (RACH) attempts while emphasizing the energy efficiency and access latency for mMTC devices with critical missions in B5G networks. Using machine learning techniques, we propose an attention-based reinforcement learning model that orchestrates the device grouping strategy to optimize device placement. Thus, the model guarantees a higher probability of success for the devices during data transmission access, eventually leading to more efficient energy consumption. Through thorough quantitative simulations, we demonstrate that the proposed learning-based approach significantly outperforms the other baseline grouping methods.

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Equilibrated and Fast Resources Allocation for Massive and Diversified MTC Services Using Multiagent Deep Reinforcement Learning

Cited by 3 publications

References 44 publications

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

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

Digital-Twin-Assisted VNF Migration Through Resource Prediction in SDN/NVF-Enabled IoT Networks

A Learning-Based Energy-Efficient Device Grouping Mechanism for Massive Machine-Type Communication in the Context of Beyond 5G Networks

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