Artificial intelligence-empowered resource management for future wireless communications: A survey

Lin, Mingxing; Zhao, Youping

doi:10.23919/jcc.2020.03.006

Cited by 52 publications

(11 citation statements)

References 13 publications

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“…ML recently elicited great attention in enabling numerous smart applications. In 6G, ML is expected to not only enable smart applications but also provide intelligent medium access control schemes and intelligent transceivers [29], [53], [54].Thus, ML can be one of the fundamental pillars of the 6G wireless network. Generally, we can divide ML into several types: traditional machine learning, federated learning, meta learning, and quantum machine learning.…”

Section: Emerging Machine Learning Schemesmentioning

confidence: 99%

6G Wireless Systems: A Vision, Architectural Elements, and Future Directions

et al. 2020

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Internet of Everything (IoE) applications such as haptics, human-computer interaction, and extended reality, using the sixth-generation (6G) of wireless systems have diverse requirements in terms of latency, reliability, data rate, and userdefined performance metrics. Therefore, enabling IoE applications over 6G requires a new framework that can be used to manage, operate, and optimize the 6G wireless system and its underlying IoE services. Such a new framework for 6G can be based on digital twins. Digital twins use a virtual representation of the 6G physical system along with the associated algorithms (e.g., machine learning, optimization), communication technologies (e.g., millimeter-wave and terahertz communication), computing systems (e.g., edge computing and cloud computing), as well as privacy and security-related technologists (e.g., blockchain). First, we present the key design requirements for enabling 6G through the use of a digital twin. Next, the architectural components and trends such as edge-based twins, cloud-based-twins, and edge-cloud-based twins are presented. Furthermore, we provide a comparative description of various twins. Finally, we outline and recommend guidelines for several future research directions.

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Section: Emerging Machine Learning Schemesmentioning

confidence: 99%

6G Wireless Systems: A Vision, Architectural Elements, and Future Directions

et al. 2020

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“…AIoT, comprising AI-empowered M-IoT, involves several mobile entities incorporating a decentralized setup offering intelligent, efficient, and reliable end-to-to-end transmissions in mission-critical scenarios [16], [17]. These networks often require a lot of reconfigurations and several load-balancing solutions, which must ensure that the resources are appropriately allocated.…”

Section: A Motivation Problem Statement and Contributionsmentioning

confidence: 99%

Optimal and Privacy-Aware Resource Management in Artificial Intelligence of Things Using Osmotic Computing

Sharma

Tan

Singh

et al. 2022

IEEE Trans. Ind. Inf.

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Critical infrastructure comprising on-demand devices, including secondary servers, comes into play when a situation like an overload is involved. The on-demand servers and devices require smart management solutions that form an integral part of Artificial Intelligence of Things (AIoT). This work considers AIoT as a combination of Mobile-Internet of Things (M-IoT) and AI requiring immediate response, secondary support system, and computational resources. Privacy in AIoT is always a concern when sharing information as intruders can eavesdrop on the settings of the system. This paper uses an osmotic computing paradigm, which enables the derivation of strategies to decide on the methods of sharing services via optimal and privacyaware resource management in AIoT. A safety competition is built on top of configuration rewards that help to attain privacyby-design. The contributions of this work are expressed using theoretical analysis and numerical simulations.

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“…In-depth and holistic coverage of DRL algorithms used for RRAM, intensive review of existing papers related to DRL for RRAM, and the coverage of more types of wireless networks Lin et al [31] Applications of AI approaches in resource management, such as spectrum, computing, and caching.…”

Section: Sections Ii/ivmentioning

confidence: 99%

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

Alwarafy¹,

Abdallah²,

Çiftler³

et al. 2021

Preprint

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<div>Next generation wireless networks are expected to be extremely complex due to their massive heterogeneity in terms of the types of network architectures they incorporate, the types and numbers of smart IoT devices they serve, and the types of emerging applications they support. In such large-scale and heterogeneous networks (HetNets), radio resource allocation and management (RRAM) becomes one of the major challenges encountered during system design and deployment. In this context, emerging Deep Reinforcement Learning (DRL) techniques are expected to be one of the main enabling technologies to address the RRAM in future wireless HetNets. In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks. Towards this, we first overview the existing traditional RRAM methods and identify their limitations that motivate the use of DRL techniques in RRAM. Then, we provide a comprehensive review of the most widely used DRL algorithms to address RRAM problems, including the value- and policy-based algorithms. The advantages, limitations, and use-cases for each algorithm are provided. We then conduct a comprehensive and in-depth literature review and classify existing related works based on both the radio resources they are addressing and the type of wireless networks they are investigating. To this end, we carefully identify the types of DRL algorithms utilized in each related work, the elements of these algorithms, and the main findings of each related work. Finally, we highlight important open challenges and provide insights into several future research directions in the context of DRL-based RRAM. This survey is intentionally designed to guide and stimulate more research endeavors towards building efficient and fine-grained DRL-based RRAM schemes for future wireless networks.</div>

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Artificial intelligence-empowered resource management for future wireless communications: A survey

Cited by 52 publications

References 13 publications

6G Wireless Systems: A Vision, Architectural Elements, and Future Directions

6G Wireless Systems: A Vision, Architectural Elements, and Future Directions

Optimal and Privacy-Aware Resource Management in Artificial Intelligence of Things Using Osmotic Computing

Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey

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