Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic

Saggese, Fabio; Pasqualini, Luca; Moretti, Marco; Abrardo, Andrea

doi:10.48550/arxiv.2103.01801

Cited by 2 publications

(3 citation statements)

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“…A DRL based network slicing algorithm is proposed in [111] to slice the total available resources between the URLLC and eMBB traffics. In the algorithm the full timefrequency resource is allocated for eMBB then an optimal policy is trained dynamically to allocate the resource by puncturing the eMBB codewords.…”

Section: ML Based Joint Schedulingmentioning

confidence: 99%

A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems

et al. 2023

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Future wireless communication is expected to be a paradigm shift from three basic service requirements of 5th Generation (5G) including enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency communication (URLLC) and the massive Machine Type Communication (mMTC). Integration of the three heterogeneous services into a single system is a challenging task. The integration includes several design issues including scheduling network resources with various services. Specially, scheduling the URLLC packets with eMBB and mMTC packets need more attention as it is a promising service of 5G and beyond systems. It needs to meet stringent Quality of Service (QoS) requirements and is used in time-critical applications. Thus through understanding of packet scheduling issues in existing system and potential future challenges is necessary. This paper surveys the potential works that addresses the packet scheduling algorithms for 5G and beyond systems in recent years. It provides state of the art review covering three main perspectives such as decentralised, centralised and joint scheduling techniques. The conventional decentralised algorithms are discussed first followed by the centralised algorithms with specific focus on single and multi-connected network perspective. Joint scheduling algorithms are also discussed in details. In order to provide an in-depth understanding of the key scheduling approaches, the performances of some prominent scheduling algorithms are evaluated and analysed. This paper also provides an insight into the potential challenges and future research directions from the scheduling perspective.

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Section: ML Based Joint Schedulingmentioning

confidence: 99%

A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems

et al. 2023

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“…The simulation results for different slices show that this method has a higher transmission rate and SIR close to the optimal method compared to static and optimal (selection of suitable sub-channel with exhaustive search) methods. In [89], a slice-based virtual resource scheduling scheme for eMBB and uRLLC based on PPO is proposed for allocating sub-carriers to uRLLC and eMBB slices, with the aim to minimize the latency for uRLLC packages. To maximize the rate and realtime response to uRLLC slices, the eMBB packets are punctured and the uRLLC packets are embedded in the eMBB packets.…”

Section: ) Resource Sharingmentioning

confidence: 99%

“…In recent years, the use of distributed methods (such as transfer learning and federated learning) in ML has been considered due to the common challenges in centralized methods [79], [80], [81], [82], [84], [85], [86], [87], [88], [89], [90], [91], [92], [83], [93], [94], [95], [110], [111], [112], [113], [114], [115], [116], [117], [118], [136] ✓ ✓ ---- [96], [97], [98], [99], [100], [103], [104], [105], [123], [124], [135] [107], [108], [109], [126], [127], [128], [35], [129], [130] such as centralization, security, privacy, and time and computational complexity. In distributed methods, a ML mode...…”

Section: Distributed Learningmentioning

confidence: 99%

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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Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic

Cited by 2 publications

References 0 publications

A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems

A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems

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

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