A Comprehensive Survey on Radio Resource Management in 5G HetNets: Current Solutions, Future Trends and Open Issues

Agarwal, Bharat; Togou, Mohammed Amine; Ruffini, Marco; Muntean, Gabriel‐Miro

doi:10.1109/comst.2022.3207967

Cited by 41 publications

(15 citation statements)

References 176 publications

(201 reference statements)

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“…FL is also examined, as it is a distributed approach orthogonal to RL and can be combined with it, resulting in federated RL (FRL). For other types of approach, the reader may consult, e.g., [4,[7][8][9]31,32] (Table A2 in Appendix A provides some further context about each of these references). Briefly, distributed RL methodologies introduce multiple agents to manage the network resources, with each one performing actions on the network to maximize a long-term reward in a cooperative or competitive fashion.…”

Section: Overview Benchmarking Criteria and Methodological Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Distributed Machine Learning and Native AI Enablers for End-to-End Resources Management in 6G

Karachalios,

Zafeiropoulos,

Kontovasilis

et al. 2023

Electronics

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6G targets a broad and ambitious range of networking scenarios with stringent and diverse requirements. Such challenging demands require a multitude of computational and communication resources and means for their efficient and coordinated management in an end-to-end fashion across various domains. Conventional approaches cannot handle the complexity, dynamicity, and end-to-end scope of the problem, and solutions based on artificial intelligence (AI) become necessary. However, current applications of AI to resource management (RM) tasks provide partial ad hoc solutions that largely lack compatibility with notions of native AI enablers, as foreseen in 6G, and either have a narrow focus, without regard for an end-to-end scope, or employ non-scalable representations/learning. This survey article contributes a systematic demonstration that the 6G vision promotes the employment of appropriate distributed machine learning (ML) frameworks that interact through native AI enablers in a composable fashion towards a versatile and effective end-to-end RM framework. We start with an account of 6G challenges that yields three criteria for benchmarking the suitability of candidate ML-powered RM methodologies for 6G, also in connection with an end-to-end scope. We then proceed with a focused survey of appropriate methodologies in light of these criteria. All considered methodologies are classified in accordance with six distinct methodological frameworks, and this approach invites broader insight into the potential and limitations of the more general frameworks, beyond individual methodologies. The landscape is complemented by considering important AI enablers, discussing their functionality and interplay, and exploring their potential for supporting each of the six methodological frameworks. The article culminates with lessons learned, open issues, and directions for future research.

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Section: Overview Benchmarking Criteria and Methodological Frameworkmentioning

confidence: 99%

“…We do not deal with such aspects here, and instead concentrate on the core RM task. Readers interested in additional aspects are referred to, e.g., [6][7][8]31,32].…”

Section: Radio Resources and Power Allocationmentioning

confidence: 99%

Distributed Machine Learning and Native AI Enablers for End-to-End Resources Management in 6G

Karachalios,

Zafeiropoulos,

Kontovasilis

et al. 2023

Electronics

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“…Recent advancements in RRM focus on addressing the complexities and dynamic nature of next-generation cellular networks [11]. In this landscape, Machine Learning (ML) emerges as a powerful tool to enhance RRM, adapting to varying network conditions [12].…”

Section: Related Workmentioning

confidence: 99%

Radio Resource Management for Intelligent Neutral Host (INH) in Multi-Operator Environments

Navidan,

Naseri,

Moerman

et al. 2024

IEEE Open J. Commun. Soc.

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This work was executed within the imec.ICON 5GECO project, a research project bringing together academic research partners and industry partners. The 5GECO project was co-financed by imec and received support from Flanders Innovation Entrepreneurship (project nr. HBC.2021.0673). This work is also funded by the Horizon Europe program under the MCSA Staff Exchanges 2021 grant agreement 101086218 (EVOLVE project).

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“…The H-CRAN framework, which combines HetNets [17] and C-RAN to achieve energy efficiency [6], is introduced in this section. Figure 1 illustrates the three ways in which energy savings are achieved in the proposed H-CRAN framework.…”

Section: Proposed Energy Efficient 5g C-ran Frameworkmentioning

confidence: 99%

"An Energy-Efficient Algorithm for Resource Allocation in H-CRAN((EE H-CRAM)) for 5G Networks"

Sahu

Sahu²

2023

Preprint

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The demand for faster internet has increased rapidly in today's generation due to more people using it. To keep up with this growth, a new network called 5G is being developed to replace the current 4G network. 5G has been designed to meet all the communication needs of users. One promising technology for this new network is Heterogeneous Cloud Radio Access Network (H-CRAN), which is energy-efficient and can handle high data rates. By using this network, better service quality can be achieved by turning small cells into bigger ones. This network is different from the usual networks we have today, because it uses a type of radio system that is much better at reusing radio resources. The structure of this network includes more Remote Radio Heads mounted around the Macro Base Station. This type of network is very helpful in saving energy and achieving high Energy Efficiency compared to regular networks. Its resource allocation techniques can optimize the limited spectrum resources by reducing the interference between users. Traditional cellular networks have not been able to save energy effectively, but this network is seen as a promising solution to the challenges faced by 5G networks, such as high data rates, limited power, and delays. In our paper, we suggest some ways to make H-CRAN more energy-efficient. We propose energy-efficient methods for H-CRAN (EE H-CRAM), incorporating an energy-efficient resource allocation management design that caters to the heterogeneous cloud radio access networks in 5G. We use a special energy-saving program to manage the resources of the network, which is designed for 5G. By using this program, we can improve the speed of the network. Overall, our paper proposes an innovative and energy-efficient approach to H-CRAN design, which can significantly improve the performance of the 5G network.

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A Comprehensive Survey on Radio Resource Management in 5G HetNets: Current Solutions, Future Trends and Open Issues

Cited by 41 publications

References 176 publications

Distributed Machine Learning and Native AI Enablers for End-to-End Resources Management in 6G

Distributed Machine Learning and Native AI Enablers for End-to-End Resources Management in 6G

Radio Resource Management for Intelligent Neutral Host (INH) in Multi-Operator Environments

"An Energy-Efficient Algorithm for Resource Allocation in H-CRAN((EE H-CRAM)) for 5G Networks"

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