Resource orchestration in network slicing using GAN-based distributional deep Q-network for industrial applications

Gupta, Rajen K.; Mahajan, Shashwat; Misra, Rajiv

doi:10.1007/s11227-022-04867-9

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…With the advent of network slicing in 5G and future networks, customized management and allocation of resources have become achievable, addressing the varied demands of diverse applications. In [21], network slicing was explored within a radio access network framework that included IIoT devices, involving base stations that share the same physical infrastructure. A deep reinforcement learningbased technique was used for resource orchestration, designed to adapt to the variable service demands reflecting the environment's state value and the corresponding actions in resource allocation.…”

Section: ) Resource Orchestration Using Gansmentioning

confidence: 99%

G-GANS for Adaptive Learning in Dynamic Network Slices

Alanazi

2024

Eng. Technol. Appl. Sci. Res.

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This paper introduces a novel approach to improve security in dynamic network slices for 5G networks using Graph-based Generative Adversarial Networks (G-GAN). Given the rapidly evolving and adaptable nature of 5G network slices, traditional security mechanisms often fall short in providing real-time, efficient, and scalable defense mechanisms. To address this gap, this study proposes the use of G-GAN, which combines the strengths of Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs) for adaptive learning and anomaly detection in dynamic network environments. The proposed approach utilizes GAN to generate realistic network traffic patterns, both normal and adversarial, whereas GNNs analyze these patterns within the context of the network's graph-based topology. This combination facilitates the early detection of anomalies and potential security threats, adapting to the ever-changing configurations of network slices. The current study presents a comprehensive methodology for implementing G-GAN, including system architecture, data processing, and model training. The experimental analysis demonstrates the efficacy of G-GAN in accurately identifying security threats and adapting to new scenarios, revealing that G-GAN outperformed established models with an accuracy of 97.12%, precision of 96.20%, recall of 97.24%, and F1-Score of 96.72%. This study not only contributes to the field of network security in the context of 5G, but also opens avenues for future exploration in the application of hybrid AI models for real-time security across various domains.

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Section: ) Resource Orchestration Using Gansmentioning

confidence: 99%

G-GANS for Adaptive Learning in Dynamic Network Slices

Alanazi

2024

Eng. Technol. Appl. Sci. Res.

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“…The proposed approach involves the simultaneous optimization of subcarrier allocation, base station transmit beamforming, and phase shift adjustments of the IRS to maximize the overall system sum rate. In Reference [61], the authors explore the potential of DQN resource orchestration techniques in managing and allocating resources within a RAN that contains IIoT devices, such as base stations that share physical infrastructure. The authors introduce generative adversarial network‐based deep distributional noisy Q‐networks (GAN‐NoisyNet) and dueling GAN‐NoisyNet to achieve variable service demands and maximize the optimal policy for IIoT 62 reward by optimizing system throughput, spectral efficiency (SE), service level agreement (SLA), optimizing both power consumption and transmission delay while maintaining a high packet transmission rate.…”

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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