Mixed-Numerology Interference-Aware Spectrum Allocation for eMBB and URLLC Network Slices

Zambianco, Marco; Verticale, Giacomo

doi:10.1109/medcomnet52149.2021.9501277

Cited by 8 publications

(9 citation statements)

References 12 publications

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“…So, these methods are not practically feasible in real-time applications with regard to the new architectures of RAN. In some proposals such as [40], [120], [136], [151], [133], [141], [147], [143], [153], [144], [145], [146], [149], [150], [155], [142], [154], [118], [152], [157] computational and time complexity have been considered as an important issue, but in other reviewed proposals this issue has not been accounted for RAN slicing. Therefore, given the changes in the network and responding to users' requests at an acceptable time, this is a vital challenge that must be considered.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

“…The simulation result shows that FL-CA performs better than AC and greedy power allocation methods in terms of spectrum efficiency. In [145], [146], two methods based on multi-branch dueling Q-network (MBDQN) are proposed for allocating radio resources to solve inter-numerology interference and maximize network throughput. First, the resource allocation problem is modeled as a non-convex integer problem and then solved by MBDQN.…”

Section: ) Resource Sharing and Algorithmic Aspects Of Resource Alloc...mentioning

confidence: 99%

“…The authors in [157] address mobility management and dynamic slice creation and management, along with the listed challenges in Section V-D1. In [157], a distributed method [141], [143], [144], [145], [146], [147], [142], [153], [120], [40], [155], [157] ✓ called UC-PA-RA is provided to allocate power and radio blocks to user devices within clusters in multiple F-APs into a single F-RAN. In this method, users' devices in a specific cluster of a F-AP can receive their information through similar RBs.…”

Section: ) Mobility Management and Dynamic Slice Creation And Managementmentioning

confidence: 99%

“…Also, the use of federated learning distributes the resource allocation algorithm between different entities in the RAN and reduces the computational and time complexity of the algorithm [144], [155]. In [40], [120], [143], [153], [145], [146], [142], [133], the speed of resource allocation to users of slices in RAN increases by distributing the resource allocation algorithm between different agents, also, the running of each agent on separate CPU thread can facilitate to create and manage the slices.…”

Section: ) Lessons Learnedmentioning

confidence: 99%

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Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

et al. 2022

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One of the key foundations of 5 th Generation (5G) and beyond 5G (B5G) networks is network slicing, in which the network is partitioned into several separated logical networks, taking into account the requirements of diverse applications. In this context, resource management is of great importance to instantiate and operate network slices and meet their performance and functional requirements. Resource management in Radio Access Networks (RANs) is associated with a range of challenges due to network dynamics and the specific requirements of each application while ensuring performance isolation. In this paper, we present a survey on state-of-the-art works that employ Machine Learning (ML) techniques in RAN slicing. We begin by reviewing the challenges, then we review the existing papers on resource management in a comprehensive manner, and classify the papers based on the used ML algorithm, the addressed challenges, and the type of allocated resources. We evaluate the maturity of current methods and state a number of open challenges and some solutions to address these challenges in RAN resource management.

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Section: Discussion and Future Research Directionsmentioning

confidence: 99%

Section: ) Resource Sharing and Algorithmic Aspects Of Resource Alloc...mentioning

confidence: 99%

Section: ) Mobility Management and Dynamic Slice Creation And Managementmentioning

confidence: 99%

Section: ) Lessons Learnedmentioning

confidence: 99%

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Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

et al. 2022

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“…The work in [21] aims to maximize the minimum expected achieved rate of eMBB users and fairness between them by employing a one-to-one matching game to compute appropriate eMBB and URLLC pairs for URLLC resource allocation. The authors of [22] and [23] aim at maximizing the aggregated throughput of the eMBB and URLLC users while mitigating the Inter-Numerology Interference (INI). They consider satisfying the minimum acceptable throughput of the eMBB and maximum allowed delay of the URLLC users according to their corresponding service requirements.…”

Section: A Related Workmentioning

confidence: 99%

Beyond 5G Resource Slicing With Mixed-Numerologies for Mission Critical URLLC and eMBB Coexistence

Esmaeily

Mendis

Mahmoodi

et al. 2023

IEEE Open J. Commun. Soc.

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Network slicing has been a significant technological advance in the 5G mobile network allowing delivery of diverse and demanding requirements. The slicing grants the ability to create customized virtual networks from the underlying physical network, while each virtual network can serve a different purpose. One of the main challenges yet is the allocation of resources to different slices, both to best serve different services and to use the resources in the most optimal way. In this paper, we study the radio resource slicing problem for Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile Broadband (eMBB) as two prominent use cases. The URLLC and eMBB traffic is multiplexed over multiple numerologies in 5G New Radio, depending on their distinct service requirements. Therein, we present our optimization algorithm, Mixed-numerology Mini-slot based Resource Allocation (MiMRA), to minimize the impact on eMBB data rate due to puncturing by different URLLC traffic classes. Our strategy controls such impact by introducing a puncturing rate threshold. Further, we propose a scheduling mechanism that maximizes the sum rate of all eMBB users while maintaining the minimum data rate requirement of each eMBB user. The results obtained by simulation confirm the applicability of our proposed resource allocation algorithm.

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Interference management in 5G and beyond networks: A comprehensive survey

Trabelsi,

Chaari Fourati,

Chen

2024

Computer Networks

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Mixed-Numerology Interference-Aware Spectrum Allocation for eMBB and URLLC Network Slices

Cited by 8 publications

References 12 publications

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

Applications of Machine Learning in Resource Management for RAN-Slicing in 5G and Beyond Networks: A Survey

Beyond 5G Resource Slicing With Mixed-Numerologies for Mission Critical URLLC and eMBB Coexistence

Interference management in 5G and beyond networks: A comprehensive survey

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