AI-Enabled Future Wireless Networks: Challenges, Opportunities, and Open Issues

Elsayed, Medhat; Erol‐Kantarci, Melike

doi:10.1109/mvt.2019.2919236

Cited by 121 publications

(51 citation statements)

References 16 publications

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“…Moreover, the full potential of UAV networks should also be exploited to optimize computing, caching, and energy resources. Also, in 6G mobile networks, since UAVs will play many roles, such as aerial BSs, computing servers, and content providers, artificial intelligence (AI) techniques can be adapted to optimize their features [331]. Leveraging the distributed computing resources over the cloud computing and MEC can be also an interesting issue to analyze [264].…”

Section: B 6g Requirementsmentioning

confidence: 99%

Softwarization of UAV Networks: A Survey of Applications and Future Trends

et al. 2020

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Unmanned Aerial Vehicles (UAVs) have become increasingly important in assisting 5G and beyond 5G (B5G) mobile networks. Indeed, UAVs have all the potentials to both satisfy the ever-increasing mobile data demands of such mobile networks and provide ubiquitous connectivity to different kinds of wireless devices. However, the UAV assistance paradigm faces a set of crucial issues and challenges. For example, the network management of current UAV-assisted systems is time consuming, complicated, and carried out manually, thus causing a multitude of interoperability issues. To efficiently address all these issues, Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two promising technologies to efficiently manage and improve the UAV assistance for the next generation of mobile networks. In the literature, no clear guidelines are describing the different use cases of SDN and NFV in the context of UAV assistance to terrestrial networks, including mobile networks. Motivated by this fact, in this survey, we guide the reader through a comprehensive discussion of the main characteristics of SDN and NFV technologies. Moreover, we provide a thorough analysis of the different classifications, use cases, and challenges related to UAV-assisted systems. We then discuss SDN/NFV-enabled UAV-assisted systems, along with several case studies and issues, such as the involvement of UAVs in cellular communications, monitoring, and routing, to name a few. We furthermore present a set of open research challenges, high-level insights, and future research directions related to UAV-assisted systems.

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Section: B 6g Requirementsmentioning

confidence: 99%

Softwarization of UAV Networks: A Survey of Applications and Future Trends

et al. 2020

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“…The relatively long convergence time of DL algorithms makes it challenging for implementing in the highly dynamic channel, traffic, and mobility conditions of C-RAN [120]. The continuous nature of user data can make the learning time infinite for the DL algorithms.…”

Section: ) Limitations and Challengesmentioning

confidence: 99%

A Survey on Applications of Deep Learning in Cloud Radio Access Network

Rodoshi

Choi

2021

IEEE Access

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The necessity for high-speed and low-latency connectivity of the vast number of mobile users is rising with the immense usage of mobile applications. A cloud radio access network (C-RAN) is a promising framework for next-generation cellular communication, which can satisfy the requirements of significantly increasing data traffic and user demands. In C-RAN, the data processing unit can be centralized and virtualized in data centers and can be shared among distributed base stations. Deep learning (DL) appears to be a feasible approach for facilitating the data processing capability, resource management in the cloud, and predicting dynamic traffic in cellular communication. The convergence of C-RAN and DL is expected to bring new prospects to both interdisciplinary research and industrial applications. In this regard, different approaches have been proposed for DL-based C-RAN in the literature. This article provides a comprehensive survey of the state-of-the-art DL techniques applied in C-RAN. A brief introduction of the C-RAN architecture and DL techniques is given to provide insights into these two emerging technologies. Existing surveys are also discussed to highlight the research gap. The reviewed works are categorized into power consumption optimization, network performance maximization, and QoS maximization based on their optimization objectives. The key ideas of DL applied in the reviewed schemes are also mentioned, and the performance evaluation techniques used in the research are discussed and compared. Lastly, research challenges and open research issues are highlighted to provide future research directions.INDEX TERMS Cloud radio access network, deep learning, evaluation techniques, optimization objectives, performance metrics.

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“…A similarly high convergence time to reach its equilibrium state was reported in [86], where the resource and power allocation algorithms proposed are based on game theory and solved by machine learning. In future wireless communication, the usage of artificial intelligence (AI) techniques is an anticipated resource allocation approach as the RRM complexity increases [95].…”

Section: ) Computational Complexitymentioning

confidence: 99%

Survey of Radio Resource Management in 5G Heterogeneous Networks

et al. 2020

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Radio resource management (RRM) for future fifth-generation (5G) heterogeneous networks (HetNets) has emerged as a critical area due to the increased density of small-cell networks and radio access technologies. Recent research has mostly concentrated on resource management, including spectrum utilization and interference mitigation, but the complexities of these resources have been given little attention. This paper provides an overview of the issues arising from future 5G systems and highlights their importance. The different approaches used in recently published surveys categorizing RRM schemes are discussed, and the survey method is presented. We report on a survey of HetNet RRM schemes that have been studied recently, with a focus on the joint optimization of radio resource allocation with other mechanisms. These RRM schemes were subcategorized according to their optimization metrics and qualitatively analyzed and compared. An analysis of the complexity of RRM schemes in terms of implementation and computation is presented. Several potential scopes of research for future RRM in 5G HetNets are also identified.

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AI-Enabled Future Wireless Networks: Challenges, Opportunities, and Open Issues

Cited by 121 publications

References 16 publications

Softwarization of UAV Networks: A Survey of Applications and Future Trends

Softwarization of UAV Networks: A Survey of Applications and Future Trends

A Survey on Applications of Deep Learning in Cloud Radio Access Network

Survey of Radio Resource Management in 5G Heterogeneous Networks

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