Performance Analysis of Evolved RAN Architectures with Open Interfaces

Vlahov, Atanas; Kougioumtzidis, Georgios; Mihovska, Albena; Poulkov, Vladimir

doi:10.13052/jmm1550-4646.19112

Cited by 3 publications

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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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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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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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6G-based Intelligent, Context-Aware, and Trustworthy User-Centric Healthcare Applications

Mihovska,

Vlahov,

Poulkov

2024

2024 Wireless Telecommunications Symposium (WTS)

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Performance Analysis of Evolved RAN Architectures with Open Interfaces

Cited by 3 publications

References 0 publications

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

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

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

6G-based Intelligent, Context-Aware, and Trustworthy User-Centric Healthcare Applications

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