Towards Real-Time User QoE Assessment via Machine Learning on LTE Network Data

Hashmi, Umair Sajid; Rudrapatna, Ashok N.; Zhao, Zhengxue; Rozwadowski, Marek; Kang, Joseph H.; Wuppalapati, Raj; Imran, Ali

doi:10.1109/vtcfall.2019.8891552

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…However, in dynamic networks where spectrum is cognitively used to adapt to spike demand, this approach is no longer fit for purpose. Proactive network based on real-time data is needed for the consumers of today and tomorrow [4], [5]. The latter approach uses machine learning to directly infer QoE from diverse QoS data, but cannot capture the nature and context of the human experience.…”

Section: A Related Workmentioning

confidence: 99%

Federated Learning of Wireless Network Experience Anomalies Using Consumer Sentiment

Guo

Jin

Sun

et al. 2023

2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

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In wireless networks, consumer experience is important for both short monitoring of the Quality of Experience (QoE) as well as long term customer retainment. Current 4G and 5G networks are not equipped to measure QoE in an automated way, and experience is still reported through traditional customer care and drive-testing. In recent years, large-scale social media analytics has enabled researchers to gather statistically significant data on consumer experience and correlate them to major events such as social celebrations or significant network outages. However, the translational pathway from languages to topicspecific emotions (e.g., sentiment) to detecting anomalies in QoE is challenging. This challenge lies in two issues: (1) the social experience data remains sparsely distributed across space, and (2) anomalies in experience jump across sub-topic spaces (e.g., from data rate to signal strength).Here, we solved these two challenges by examining the spectral space of experience across topics using federated learning (FL) to identify anomalies. This can inform telecom operators to pay attention to potential network demand or supply issues in real time using relatively sparse and distributed data. We use real social media data curated for our telecommunication projects across London and the United Kingdom to demonstrate our results. FL was able to achieve 74-92% QoE anomaly detection accuracy, with the benefit of 30-45% reduce data transfer and preserving privacy better than raw data transfer.

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Section: A Related Workmentioning

confidence: 99%

Federated Learning of Wireless Network Experience Anomalies Using Consumer Sentiment

Guo

Jin

Sun

et al. 2023

2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

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“…In wireless communication systems, AI based techniques have been recognized as one of the most suitable computational paradigm for improving the operational efficiency. For instance, AI-based solutions have shown significant performance improvements for network security [95], resource management [96,97], activity forecasting [98], user quality of experience [99] and self-organization capabili ties [100]. For RISassisted networks, AI based techniques are quite useful to solve the important signal processing issues, such as channel estimation, phase-shift optimization and resource allocation, due to their ability to learn underlying trends between the operating and efficiency parameters for large search space.…”

Section: Ai-empowered Ris-assisted Networkmentioning

confidence: 99%

Exploring reconfigurable intelligent surfaces for 6G: State‐of‐the‐art and the road ahead

et al. 2022

Self Cite

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Reconfigurable intelligent surfaces (RISs) are envisioned to transform the propagation space into a smart radio environment (SRE) to realize the diverse applications of sixthgeneration (6G) wireless communication. By smartly tuning the massive number of elements via controller, an RIS can passively phase-shift the electromagnetic (EM) waves to enhance the system performance. The absence of radio-frequency (RF) chains makes RIS an energy-efficient and cost-effective solution for future wireless networks. In this paper, the state-of-the-art research on different aspects of RIS-assisted communication is explored. Specifically, the fundamentals of RIS are first introduced, including the RIS's structure, operating principle, and deployment strategies. The emerging applications of RISs are then comprehensively discussed for 6G wireless networks. In addition, the crucial challenges for RIS-assisted networks are elaborated, namely, RIS channel state information (CSI) acquisition and passive beamforming optimization. Furthermore, the recent research contributions leveraging the artificial intelligence (AI) based techniques for channel estimation, phase-shift optimization, and resource allocation in RIS-assisted networks are presented. Finally, to provide effective guidance for future research, important research directions for realizing RIS-assisted network are highlighted.

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“…In wireless communication systems, AI based techniques have been recognized as one of the most suitable computational paradigm for improving the operational efficiency. For instance, AI based solutions have shown significant performance improvements for network security [97], resource management [98], [99], activity forecasting [100], user quality of experience [101] and self-organization capabilities [102]. For RIS-assisted networks, AI based techniques are quite useful to solve the important signal processing issues, such as channel estimation, phase-shift optimization and resource allocation, due to their ability to learn underlying trends between the…”

Section: Ai-empowered Ris-assisted Networkmentioning

confidence: 99%

Exploring Reconfigurable Intelligent Surfaces for 6G: State-of-the-Art and the Road Ahead

Basharat¹,

Khan²,

Iqbal³

et al. 2022

Preprint

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Reconfigurable intelligent surfaces (RISs) are envisioned to transform the propagation space into a smart radio environment (SRE) to realize the diverse applications of sixth-generation (6G) wireless communication. By smartly tuning the massive number of elements via controller, an RIS can passively phase-shift the electromagnetic (EM) waves to enhance the system performance. The absence of radio-frequency (RF) chains makes RIS an energy-efficient and cost-effective solution for future wireless networks. In this paper, we explore the state-of-the-art research on different aspects of RIS-assisted communication. Specifically, we first introduce the fundamentals of RIS, including the RIS's structure, operating principle, and deployment strategies. We then comprehensively discuss the emerging applications of RISs for 6G wireless networks. In addition, we elaborate on the crucial challenges for RIS-assisted networks, namely, RIS channel state information (CSI) acquisition and passive beamforming optimization. Furthermore, we present the recent research contributions leveraging the artificial intelligence (AI) based techniques for channel estimation, phase-shift optimization, and resource allocation in RIS-assisted networks. Finally, to provide effective guidance for future research, we highlight important research directions for realizing RIS-assisted network.

show abstract

Towards Real-Time User QoE Assessment via Machine Learning on LTE Network Data

Cited by 13 publications

References 21 publications

Federated Learning of Wireless Network Experience Anomalies Using Consumer Sentiment

Federated Learning of Wireless Network Experience Anomalies Using Consumer Sentiment

Exploring reconfigurable intelligent surfaces for 6G: State‐of‐the‐art and the road ahead

Exploring Reconfigurable Intelligent Surfaces for 6G: State-of-the-Art and the Road Ahead

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