Intelligent and QoE-aware Open Radio Access Networks

Kougioumtzidis, Georgios; Poulkov, Vladimir; Zaharis, Zaharias D.; Lazaridis, Pavlos I.

doi:10.23919/at-ap-rasc54737.2022.9814435

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Open RAN technology has the potential to revolutionize mobile RAN and is expected to be a key element in the creation of next-generation wireless networks. In line with the O-RAN Alliance specifications, the Open RAN architecture will be able to provide customized communication services to meet the demanding and diverse service demands of innovative usage scenarios, including online and cloud gaming applications [39].…”

Section: A Open Ran Testbedmentioning

confidence: 99%

QoE Prediction for Gaming Video Streaming in O-RAN Using Convolutional Neural Networks

Kougioumtzidis,

Vlahov,

Poulkov

et al. 2024

IEEE Open J. Commun. Soc.

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The growing popularity of online and cloud gaming applications is reshaping the landscape of the entertainment industry and acting as a key driver of market growth. However, the dependency of these applications on network resources poses significant challenges to the communication infrastructure. This is particularly critical as network performance plays a key role in influencing user satisfaction during gameplay. Inevitably, these inherently interactive applications are also closely linked to the concept of quality of experience (QoE), which expresses the perceived quality of a service by end-users. In this paper, we leverage deep learning methodologies to develop an objective QoE prediction model. Specifically, the proposed prediction model investigates the effect of wireless network operation on the QoE of gaming video streaming. Employing a tailored multi-headed convolutional neural network (multi-headed CNN), the model can predict in real-time the transmission-related QoE value using measurable quality of service (QoS) parameters. To validate the effectiveness of the model, tests and evaluations were conducted in an open radio access network testbed environment equipped with O-RAN-compatible interfaces.

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Section: A Open Ran Testbedmentioning

confidence: 99%

QoE Prediction for Gaming Video Streaming in O-RAN Using Convolutional Neural Networks

Kougioumtzidis,

Vlahov,

Poulkov

et al. 2024

IEEE Open J. Commun. Soc.

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“…The [37]. The Open RAN experimental network is built on a small-scale testbed supporting the 4G architecture as well as the 5G non-standalone (NSA) architecture, which uses a combination of the legacy 4G long-term evolution (LTE) infrastructure with a 5G RAN.…”

Section: Qoe Prediction Modelmentioning

confidence: 99%

Deep Learning-Aided QoE Prediction for Virtual Reality Applications Over Open Radio Access Networks

Kougioumtzidis,

Vlahov,

Poulkov

et al. 2023

IEEE Access

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Nowadays, innovative applications in the field of virtual reality (VR) are being developed, attracting the interest of both academia and industry. Wireless VR applications focus on various aspects of daily life, such as smart education, entertainment, healthcare, tourism, architecture, automotive, and industrial automation. All these inherently interactive applications that aim to create immersive experiences for users are closely related to the concept of quality of experience (QoE), which expresses the quality of a service as perceived by end-users. In this paper, we develop an objective QoE prediction model based on deep learning techniques. The prediction model examines the impact of wireless network operation on the quality of VR 360-degree video streaming. It is based on an encoder-decoder long short-term memory (LSTM) neural network and is able to predict in real-time the overall transmission-related QoE value using only measurable quality of service (QoS) parameters. The prediction model is tested and evaluated on an open radio access network testbed with interfaces based on the O-RAN specifications. INDEX TERMSDeep learning, deep neural network (DNN), long short-term memory (LSTM), open radio access network (Open RAN), quality of experience (QoE), QoE prediction, recurrent neural network (RNN), virtual reality (VR).

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“…3) Quality of Service: The Quality of Service (QoS) will be a vital element in the Open RAN system, determining the system's performance level regarding user experience. Network operations management, proper hardware-software selection, and continuous monitoring of app-level services are some of the fundamental decisions that contribute to improved QoS [28]. One of the challenges in the Open RAN system is identifying front-haul open interface (connection between O-DU and O-RU) requirements to achieve ultra-low latency [29].…”

Section: B Performancementioning

confidence: 99%

Open Radio Access Network challenges for Next Generation Mobile Network

Aryal

Bertin

Crespi

2023

2023 26th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)

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Due to the tightly-coupled hardware and software architecture of existing RAN systems and their non-flexibility, disaggregation of software and hardware can bring many unprecedented opportunities regarding enabling the entrance of multiple small-scale infrastructure providers to enter the RAN market, which creates more competitive and innovative RAN ecosystem. Moreover, mobile network operators (MNOs) will also have the advantage of selecting the services according to their network requirements. Open Radio Access Network (O-RAN) builds a multi-vendor RAN ecosystem and utilizes openness and intelligence to address the complexity of network functionalities, increase development agility, and provide more cost-effective platforms due to softwarization and the avoidance of dedicated hardware. However, O-RAN faces many challenges, such as interoperability, convergence, and AI/ML management which still need to be addressed before its wide deployment. This paper surveys the existing issues in the Open RAN ecosystem and explores existing solutions.

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Intelligent and QoE-aware Open Radio Access Networks

Cited by 5 publications

References 6 publications

QoE Prediction for Gaming Video Streaming in O-RAN Using Convolutional Neural Networks

QoE Prediction for Gaming Video Streaming in O-RAN Using Convolutional Neural Networks

Deep Learning-Aided QoE Prediction for Virtual Reality Applications Over Open Radio Access Networks

Open Radio Access Network challenges for Next Generation Mobile Network

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