On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration

Rezazadeh, Farhad; Zanzi, Lanfranco; Devoti, Francesco; Chergui, Hatim; Costa‐Pérez, Xavier; Verikoukis, Christos

doi:10.1109/tvt.2022.3218158

Cited by 22 publications

(13 citation statements)

References 52 publications

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“…The challenge of disaggregation of elements in O-RAN is addressed in Refs. [9,10]. References [11,12] proposed an advance method related to the RL, where an agent is learning from its behavior and dynamically partitions the jobs in O-RAN, aiming to minimize the ingesting of power in RAN.…”

Section: Related Workmentioning

confidence: 99%

AI/ML Enabled Automation System for Software Defined Disaggregated Open Radio Access Networks: Transforming Telecommunication Business

Kumar

2024

Big Data Min. Anal.

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Open Air Interface (OAI) alliance recently introduced a new disaggregated Open Radio Access Networks (O-RAN) framework for next generation telecommunications and networks. This disaggregated architecture is open, automated, software defined, virtual, and supports the latest advanced technologies like Artificial Intelligence (AI) Machine Learning (AI/ML). This novel intelligent architecture enables programmers to design and customize automated applications according to the business needs and to improve quality of service in fifth generation (5G) and Beyond 5G (B5G). Its disaggregated and multivendor nature gives the opportunity to new startups and small vendors to participate and provide cheap hardware software solutions to keep the market competitive. This paper presents the disaggregated and programmable O-RAN architecture focused on automation, AI/ML services, and applications with Flexible Radio access network Intelligent Controller (FRIC). We schematically demonstrate the reinforcement learning, external applications (xApps), and automation steps to implement this disaggregated O-RAN architecture. The idea of this research paper is to implement an AI/ML enabled automation system for software defined disaggregated O-RAN, which monitors, manages, and performs AI/ML-related services, including the model deployment, optimization, inference, and training.

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Section: Related Workmentioning

confidence: 99%

AI/ML Enabled Automation System for Software Defined Disaggregated Open Radio Access Networks: Transforming Telecommunication Business

Kumar

2024

Big Data Min. Anal.

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“…These VNFs run a diverse range of services for users, and each VNF incorporates an agent to adjust the allocated resources, physical host, and connections to form different types of slices to provide users with differentiated services. Here, we consider the RAN part is built under the Open-RAN paradigm realized by the VNFs at ESs with open standards and interfaces, which is witnessed to have more advantages in maintaining the network and reducing the cost by enhancing the flexibility and scalability of the future 6G system [37]. Besides, the BSs are also connected to their neighbors by BS-to-BS links and to the remote cloud by backhaul links, these links are generally built with highspeed optical fibers and have sufficient transmission speed.…”

Section: A System Modelmentioning

confidence: 99%

“…Thus, the complexity of Algorithm 3, as well as the overall framework can be expressed as Compared to other baselines without distributed paradigm integration [1], [15], the complexity of the proposed FL-based scheme does not directly increase with the number of slices. Considering the scalability issue in the 6G system [37], the proposed scheme will be feasible to deploy slices in scenarios with a relatively stable number of BSs and UEs.…”

Section: E Complexity Analysismentioning

confidence: 99%

Federated Deep Reinforcement Learning for Prediction-Based Network Slice Mobility in 6 G Mobile Networks

Ming,

Yu,

Taleb

2024

IEEE Trans. on Mobile Comput.

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Network slices are generally coupled with services and face service continuity/unavailability concerns due to the high mobility and dynamic requests from users. Network slice mobility (NSM), which considers user mobility, service migration, and resource allocation from a holistic view, is witnessed as a key technology in enabling network slices to respond quickly to service degradation. Existing studies on NSM either ignored the trigger detection before NSM decision-making or didn't consider the prediction of future system information to improve the NSM performance, and the training of deep reinforcement learning (DRL) agents also faces challenges with incomplete observations. To cope with these challenges, we consider that network slices migrate periodically and utilize the prediction of system information to assist NSM decision-making. The periodical NSM problem is further transformed into a Markov decision process, and we creatively propose a prediction-based federated DRL framework to solve it. Particularly, the learning processes of the prediction model and DRL agents are performed in a federated learning paradigm. Based on extensive experiments, simulation results demonstrate that the proposed scheme outperforms the considered baseline schemes in improving long-term profit, reducing communication overhead, and saving transmission time.

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“…Despite its novelty, this work does not consider the transmission delay in the air interface. In [26], the authors propose an iterative algorithm to address the joint radio resource and power allocation problem, while the authors of [27] solve this problem by federated learning. In spite of their significance, their findings focus on the average delay as the key parameter.…”

Section: Related Workmentioning

confidence: 99%

A Stochastic Network Calculus (SNC)-Based Model for Planning B5G uRLLC RAN Slices

Adamuz-Hinojosa

Sciancalepore²,

Ameigeiras

et al. 2023

IEEE Trans. Wireless Commun.

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Radio Access Network (RAN) slicing involves several challenges. In particular, the Mobile Network Operator (MNO) must ensure -before deploying each slice-that corresponding requirements can be met throughout its lifetime. For ultra-Reliable Low Latency Communication (uRLLC) slices, the MNO must guarantee the packet transmission delay within a delay budget with a certain probability. Most existing solutions focus on allocating dynamically radio resources to maximize the number of packets, whose transmission delay is within the delay budget. However, these solutions do not ensure the violation probability is below a target value in the long term. In this paper, we focus on slicing from a planning perspective. Specifically, we propose a Stochastic Network Calculus (SNC)-based model, which given the amount of radio resources allocated for a uRLLC slice, the target violation probability and the traffic demand distribution, provides the delay bound for such conditions. Additionally, we propose heuristics for planning uRLLC slices. Interestingly, such heuristics benefit from the proposed SNC-based model to compute the amount of radio resources to be assigned to each slice while its delay bound, given a target

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On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration

Cited by 22 publications

References 52 publications

AI/ML Enabled Automation System for Software Defined Disaggregated Open Radio Access Networks: Transforming Telecommunication Business

AI/ML Enabled Automation System for Software Defined Disaggregated Open Radio Access Networks: Transforming Telecommunication Business

Federated Deep Reinforcement Learning for Prediction-Based Network Slice Mobility in 6 G Mobile Networks

A Stochastic Network Calculus (SNC)-Based Model for Planning B5G uRLLC RAN Slices

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