Toward Modular and Flexible Open RAN Implementations in 6G Networks: Traffic Steering Use Case and O-RAN xApps

Dryjański, Marcin; Kułacz, Łukasz; Kliks, Adrian

doi:10.3390/s21248173

Cited by 63 publications

(33 citation statements)

References 18 publications

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“…Improved performance thus can be achieved with the UE-based approach we propose, enabled by the O-RAN architecture. In [28], the authors present a general overview on O-RAN and its potentialities by exploring the TS use case, showing the flexibility of this novel architecture and its orchestration capabilities. However, their work is more tailored to the O-RAN capabilities rather than their xApp performances.…”

Section: Dual Connectivity and Traffic Steeringmentioning

confidence: 99%

Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

Lacava¹,

Polese²,

Sivaraj³

et al. 2022

Preprint

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5G and beyond mobile networks will support heterogeneous use cases at an unprecedented scale, thus demanding automated control and optimization of network functionalities customized to the needs of individual users. Such fine-grained control of the Radio Access Network (RAN) is not possible with the current cellular architecture. To fill this gap, the Open RAN paradigm and its specification introduce an "open" architecture with abstractions that enable closed-loop control and provide data-driven, and intelligent optimization of the RAN at the userlevel. This is obtained through custom RAN control applications (i.e., xApps) deployed on near-real-time RAN Intelligent Controller (near-RT RIC) at the edge of the network. Despite these premises, as of today the research community lacks a sandbox to build data-driven xApps, and create large-scale datasets for effective Artificial Intelligence (AI) training. In this paper, we address this by introducing ns-O-RAN, a software framework that integrates a real-world, production-grade near-RT RIC with a 3GPP-based simulated environment on ns-3, enabling at the same time the development of xApps and automated large-scale data collection and testing of Deep Reinforcement Learning (DRL)driven control policies for the optimization at the user-level. In addition, we propose the first user-specific O-RAN Traffic Steering (TS) intelligent handover framework. It uses Random Ensemble Mixture (REM), a Conservative Q-learning (CQL) algorithm, combined with a state-ofthe-art Convolutional Neural Network (CNN) architecture, to optimally assign a serving base station to each user in the network. Our TS xApp, trained with more than 40 million data points collected by ns-O-RAN, runs on the near-RT RIC and controls the ns-O-RAN base stations. We evaluate the performance on a large-scale deployment with up to 126 users with 8 base stations, showing that the xApp-based handover improves throughput and spectral efficiency by an average of 50% over traditional handover heuristics, with less mobility overhead.

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Section: Dual Connectivity and Traffic Steeringmentioning

confidence: 99%

Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

Lacava¹,

Polese²,

Sivaraj³

et al. 2022

Preprint

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“…Furthermore, open-source solutions, such as Acumos AI, emerge as a potential development platform for ML models. Several AI/ML use cases are already identified by the O-RAN community, such as QoE (Quality of Experience) optimization, traffic steering [ 16 ], user access control [ 17 ] and V2X handover management [ 12 ]. It is important to highlight that although some ML applications are described in O-RAN reports, the huge potential of applying MATL in O-RAN are not covered in any of the use cases.…”

Section: Background On Disaggregated Virtualized Ran and O-ranmentioning

confidence: 99%

Multi-Agent Team Learning in Virtualized Open Radio Access Networks (O-RAN)

Rivera

Zhang

Zhou

et al. 2022

Sensors

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Starting from the concept of the Cloud Radio Access Network (C-RAN), continuing with the virtual Radio Access Network (vRAN) and most recently with the Open RAN (O-RAN) initiative, Radio Access Network (RAN) architectures have significantly evolved in the past decade. In the last few years, the wireless industry has witnessed a strong trend towards disaggregated, virtualized and open RANs, with numerous tests and deployments worldwide. One unique aspect that motivates this paper is the availability of new opportunities that arise from using machine learning, more specifically multi-agent team learning (MATL), to optimize the RAN in a closed-loop where the complexity of disaggregation and virtualization makes well-known Self-Organized Networking (SON) solutions inadequate. In our view, Multi-Agent Systems (MASs) with MATL can play an essential role in the orchestration of O-RAN controllers, i.e., near-real-time and non-real-time RAN Intelligent Controllers (RIC). In this article, we first provide an overview of the landscape in RAN disaggregation, virtualization and O-RAN, then we present the state-of-the-art research in multi-agent systems and team learning as well as their application to O-RAN. We present a case study for team learning where agents are two distinct xApps: power allocation and radio resource allocation. We demonstrate how team learning can enhance network performance when team learning is used instead of individual learning agents. Finally, we identify challenges and open issues to provide a roadmap for researchers in the area of MATL based O-RAN optimization.

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“…Furthermore, Kułacz and Kliks [26] propose the definition of policies in a file form for Dynamic Spectrum Access (DSA) function in OpenRAN networks in implementing xApps in O-RAN. [27] describes a modular xApp implementation in O-RAN for a traffic steering use case based on the Open networking approach. [28] xApps have been employed on O-RAN defined Near Real-Time Radio Intelligence Controller (NearRT RIC) for RAN optimization at the RAN edge.…”

Section: Background and Related Studiesmentioning

confidence: 99%

Network resource allocation for emergency management based on closed-loop analysis

Blessed¹,

Aliyu²,

Agajo³

et al. 2022

ITU J-FET

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The telecommunication system being a critical pillar of emergency management, intelligent deployment and management of slices in an affected area will help emergency responders. Techniques such as automated management of Machine Learning (ML) pipelines across the edge and emergency responder devices, usage of hierarchical closed-loops, and offloading inference tasks closer to the edge can minimize latencies for first responders in case of emergencies. This study describes the major results from building a Proof of Concept (PoC) for network resource allocation for emergency management using a hierarchical autonomous Artificial Intelligence (AI)/ML-based closed-loops in the mobile network, organized by the Internal Telecommunication Union Focus Group on Autonomous Networks (ITU FG-AN). The background scenario for this PoC included the interaction between a higher closed-loop in the Operations Support System (OSS) and a lower closed-loop in Radio Access Network (RAN) to intelligently share RAN resources between the public and the emergency responder slice. Representation of closed-loop "controllers" in a declarative fashion (intent), triggering "imperative actions" in the "underlay" based on the intent, setup of a data pipeline between various components, and methods of "influencing" lower layer loops using specific logic/models, were some of the essential aspects investigated by various teams. The main conclusions are summarised in this paper, including the significant observations and limitations from the PoC as well as future directions.

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Toward Modular and Flexible Open RAN Implementations in 6G Networks: Traffic Steering Use Case and O-RAN xApps

Cited by 63 publications

References 18 publications

Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures

Multi-Agent Team Learning in Virtualized Open Radio Access Networks (O-RAN)

Network resource allocation for emergency management based on closed-loop analysis

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