Edge Intelligence-Based Ultra-Reliable and Low-Latency Communications for Digital Twin-Enabled Metaverse

Huynh, Dang Van; Khosravirad, Saeed R.; Masaracchia, Antonino; Dobre, Octavia A.; Duong, Trung Q.

doi:10.1109/lwc.2022.3179207

Cited by 128 publications

(44 citation statements)

References 13 publications

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“…Furthermore, a deep learning-based scheme was proposed to optimize caching decisions for the maximization of system utility. In [25], the authors proposed a DT framework and jointly considered the communication, computing, and storage, which effectively achieves the goal of minimizing the latency performance. These studies provide useful insights in DITEN, but they mainly focus on the ground DITEN.…”

Section: Related Workmentioning

confidence: 99%

Digital Twin Assisted Task Offloading for Aerial Edge Computing and Networks

Liu

Tan

et al. 2022

IEEE Trans. Veh. Technol.

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Considering the user mobility and unpredictable mobile edge computing (MEC) environments, this paper studies the intelligent task offloading problem in unmanned aerial vehicle (UAV)-enabled MEC with the assistance of digital twin (DT). We aim at minimizing the energy consumption of the entire MEC system by jointly optimizing mobile terminal users (MTUs) association, UAV trajectory, transmission power distribution and computation capacity allocation while respecting the constraints of mission maximum processing delays. Specifically, double deep Q-network (DDQN) algorithm stemming from deep reinforcement learning is first proposed to effectively solve the problem of MTUs association and UAV trajectory. Then, the closed-form expression is employed to handle the problem of transmission power distribution and the computation capacity allocation problem is further addressed via an iterative algorithm. Numerical results show that our proposed scheme is able to converge and significantly reduce the total energy consumption of the MEC system compared to the benchmark schemes.

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Section: Related Workmentioning

confidence: 99%

Digital Twin Assisted Task Offloading for Aerial Edge Computing and Networks

Liu

Tan

et al. 2022

IEEE Trans. Veh. Technol.

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“…Equally important, extremely reliable communication should be guaranteed in DT-empowered wireless networks, in order to ensure accurate model training, and hence, precise decision-making process. Within the context of smart cities, URLLC is an essential element in deploying mission-critical applications, such as autonomous driving, remote surgery, and public safety, to name a few [8]. DT and URLLC technologies in smart city applications are intertwined, in the sense that each is an enabler and being enabled by the other.…”

Section: Urllcmentioning

confidence: 99%

Digital Twin-Empowered Smart Cities: A New Frontier of Wireless Networks

Bariah¹,

Sari²,

Debbah³

2022

Preprint

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<p>The future of wireless network generations is revolving toward unlocking the opportunities offered by virtualization and digitization services in smart city applications, with the aim to realize improved quality-of-experience (QoE) and bring several advantages to modern cities. According to the rapid development in the field of network virtualization, we envision that future smart cities will run over ubiquitous deployment of virtualized components that are controlled by artificial intelligence (AI), i.e., the conceptualization of the Digital Twin (DT) paradigm. The key principle of the DT relies on creating a holistic representation of wireless network elements, in addition to decoupling the information pertaining to physical objects and dynamics, into a cyber twin. The cyber twin will then leverage this information for AI models training, and then reasoning and decision making operations, which will be then reflected to the physical environment, for improved sustainability. Motivated by this, in this article, we put a forward-looking vision to the integration of DT in smart city applications, and the intertwined role of wireless technologies as being enablers and enabled by the DT. Moreover, we sketch the roadmap toward identifying the limitations of the DT in 6G-enabled smart cities, and open new horizons for further developments in different design aspects. </p>

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“…Some recent work focused on the performance of DT in the metaverse and proposed solutions to enhance the DT security and quality of experience [8], [9], [10]. Such solutions focus on DT service performance as a single entity in the metaverse.…”

Section: Introductionmentioning

confidence: 99%

Integrating Digital Twin and Advanced Intelligent Technologies to Realize the Metaverse

Aloqaily

Bouachir

Karray

et al. 2023

IEEE Consumer Electron. Mag.

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The advances in Artificial Intelligence (AI) have led to technological advancements in a plethora of domains. Healthcare, education, and smart city services are now enriched with AI capabilities. These technological advancements would not have been realized without the assistance of fast, secure, and fault-tolerant communication media. Traditional processing, communication and storage technologies cannot maintain high levels of scalability and user experience for immersive services. The metaverse is an immersive three-dimensional (3D) virtual world that integrates fantasy and reality into a virtual environment using advanced virtual reality (VR) and augmented reality (AR) devices. Such an environment is still being developed and requires extensive research in order for it to be realized to its highest attainable levels. In this article, we discuss some of the key issues required in order to attain realization of metaverse services. We propose a framework that integrates digital twin (DT) with other advanced technologies such as the sixth generation (6G) communication network, blockchain, and AI, to maintain continuous end-to-end metaverse services. This article also outlines requirements for an integrated, DT-enabled metaverse framework and provides a look ahead into the evolving topic.

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Edge Intelligence-Based Ultra-Reliable and Low-Latency Communications for Digital Twin-Enabled Metaverse

Cited by 128 publications

References 13 publications

Digital Twin Assisted Task Offloading for Aerial Edge Computing and Networks

Digital Twin Assisted Task Offloading for Aerial Edge Computing and Networks

Digital Twin-Empowered Smart Cities: A New Frontier of Wireless Networks

Integrating Digital Twin and Advanced Intelligent Technologies to Realize the Metaverse

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