زينه الموسوي

Abstract The relationship between humans an‎d the Internet has become deeply intertwined, to the extent that the Internet is now considered one of humanitys fundamental an‎d indispensable needs. A sudden loss of Internet connectivity would severely disrupt communication, commerce, education, an‎d social interaction, resulting in immediate an‎d long-term consequences for the economy, welfare, an‎d national stability. Therefore, the quality of Internet services (QoS) plays a vital role in shaping the performance an‎d well- being of individuals, organizations, an‎d societies. In this highly competitive environment, network operators constantly strive to deliver superior service quality to attract an‎d retain subscribers. However, with the advancement of mobile an‎d broadban‎d networks, a new paradigm known as Quality of Experience (QoE) has emergedone that not only focuses on technical service metrics but also reflects users perceived satisfaction an‎d overall experience. Unlike QoS, which can be quantified through measurable network parameters, QoE involves complex psychological an‎d contextual factors that make its accurate modeling an‎d eva‎luation highly challenging. To overcome these challenges, this research explores methods for modeling, predicting, an‎d enhancing QoE by integrating traditional performance indicators with user-centric data. The study highlights the importance of collecting real-time measurements an‎d user feedback to create dynamic an‎d adaptive quality models. By leveraging machine learning an‎d data-driven techniques, operators can identify hidden patterns, anticipate degradations in user experience, an‎d proactively optimize network performance. Furthermore, the thesis introduces various categories of QoE an‎d QoS based on the type of servicesuch as voice, video, web browsing, an‎d interactive applicationsan‎d analyzes their interdependencies. The findings of this study aim to provide a practical framework that helps operators an‎d researchers better understan‎d user expectations, bridge the gap between QoS an‎d QoE, an‎d ultimately enhance satisfaction an‎d service quality in next-generation communication networks.

(QoE كيفيت تجربه ) ، QoS )كيفيت خدمات(، چندرسان هاي، يادگيري ماشين، استاندارد ITU)

(QoE (Quality of Experience), QoS (Quality of Service), Multimedia, Machine Learning, ITU Standard)

zinah almusawi

Abolfazl Diyanat