Federated learning of wireless network experience anomalies using consumer sentiment

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dc.contributor.authorGuo, Weisi
dc.contributor.authorJin, Bailu
dc.contributor.authorSun, Schyler C.
dc.contributor.authorWu, Yue
dc.contributor.authorQi, Weijie
dc.contributor.authorZhang, Jie
dc.date.accessioned2023-03-29T11:36:12Z
dc.date.available2023-03-29T11:36:12Z
dc.date.issued2023-03-23
dc.description.abstractIn 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 topic-specific 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.en_UK
dc.description.sponsorshipEuropean Union funding: 778305en_UK
dc.identifier.citationGuo W, Jin B, Sun SC, et al., (2023) Federated learning of wireless network experience anomalies using consumer sentiment. In: 2023 5th International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 20-23 February 2023, Bali, Indonesiaen_UK
dc.identifier.eisbn978-1-6654-5645-6
dc.identifier.eissn2831-6983
dc.identifier.isbn978-1-6654-5646-3
dc.identifier.issn2831-6991
dc.identifier.urihttps://doi.org/10.1109/ICAIIC57133.2023.10067061
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19369
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectfederated learningen_UK
dc.subjectwireless networken_UK
dc.subjectquality of experienceen_UK
dc.subjectsentiment analysisen_UK
dc.subjectsocial mediaen_UK
dc.titleFederated learning of wireless network experience anomalies using consumer sentimenten_UK
dc.typeConference paperen_UK

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