Policy-Gradient-Based Reinforcement Learning for Computing Resources Allocation in O-RAN

Sharara, Mahdi; Pamuklu, Turgay; Hoteit, Sahar; Erol‐Kantarci, Melike

doi:10.1109/cloudnet55617.2022.9978863

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…[17] presents a new architecture framework called 5G Non-Public Networks (NPN). References [18][19][20][21] explore the development of MLbased closed-loop results related to the O-RAN design and demonstrate preliminary O-RAN lab setup, testing, and validation [22] . The Colosseum network simulator is utilized to deploy O-RAN, manage multiple network slices, and perform experimentation [23] .…”

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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“…Open RAN controllers can leverage the data and telemetry to understand user requirements and evolving contexts, and map them into effective configurations of the slicing and scheduling policies of the network which improve resource utilization and quality of service for users [95]. Researchers have explored the application of AI/ML-based optimization in network slicing, scheduling, and service provisioning, adapting the network to different slices and user needs [96].…”

Section: Open Ran Principle #3: Ai-based Closed-loop Controlmentioning

confidence: 99%

Empowering the 6G Cellular Architecture With Open RAN

Polese,

Dohler,

Dressler

et al. 2024

IEEE J. Select. Areas Commun.

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Innovation and standardization in 5G have brought advancements to every facet of the cellular architecture. This ranges from the introduction of new frequency bands and signaling technologies for the radio access network (RAN), to a core network underpinned by micro-services and network function virtualization (NFV). However, like any emerging technology, the pace of real-world deployments does not instantly match the pace of innovation. To address this discrepancy, one of the key aspects under continuous development is the RAN with the aim of making it more open, adaptive, functional, and easy to manage.In this paper, we highlight the transformative potential of embracing novel cellular architectures by transitioning from conventional systems to the progressive principles of Open RAN. This promises to make 6G networks more agile, cost-effective, energy-efficient, and resilient. It opens up a plethora of novel use cases, ranging from ubiquitous support for autonomous devices to cost-effective expansions in regions previously underserved. The principles of Open RAN encompass: (i) a disaggregated architecture with modular and standardized interfaces; (ii) cloudification, programmability and orchestration; and (iii) AIenabled data-centric closed-loop control and automation. We first discuss the transformative role Open RAN principles have played in the 5G era. Then, we adopt a system-level approach and describe how these Open RAN principles will support 6G RAN and architecture innovation. We qualitatively discuss potential performance gains that Open RAN principles yield for specific 6G use cases. For each principle, we outline the steps that research, development and standardization communities ought to take to make Open RAN principles central to next-generation cellular network designs.

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“…In [45], the authors consider the problem in which the eMBB and URLLC services compete for limited and insufficient computing resources, and the operator must balance the allocation of these resources to users of both services in multiple O-RUs/shared O-Cloud while maximizing fairness. The problem is initially modeled as an Integer Linear Programming (ILP) problem.…”

Section: Resource Managementmentioning

confidence: 99%

“…2022 [45] Employed a policy gradient-based RL algorithm as an alternative to the initially proposed ILP, to solve an MDP and address the challenge of fairly allocating limited resources to eMBB and URLLC users from multiple O-RUs while providing a significantly less complex solution.…”

Section: Year Ref Contributionmentioning

confidence: 99%

Recent Advances in Machine Learning for Network Automation in the O-RAN

Hamdan,

Lee,

Triantafyllopoulou

et al. 2023

Sensors

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The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.

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Policy-Gradient-Based Reinforcement Learning for Computing Resources Allocation in O-RAN

Cited by 8 publications

References 17 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

Empowering the 6G Cellular Architecture With Open RAN

Recent Advances in Machine Learning for Network Automation in the O-RAN

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