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학술대회 User Mobility Synthesis based on Generative Adversarial Networks: A Survey
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신승재, 전홍석, 조충래, 윤승현, 김태연
International Conference on Advanced Communications Technology (ICACT) 2020, pp.94-103
19HH1200, 초연결 지능 인프라 원천기술 연구개발, 김선미
User mobility characteristics are considered to significantly affect the performance and UX of modern IT systems such as mobile communications, context-aware services, location-based applications, smart mobility, and so on. Due to privacy concerns, legal issues, and expensive measurement costs for real user mobility traces, synthetic mobility traces are widely used for research and development purposes. Until now, most of the user mobility synthesis approaches have been categorized into two major paradigms: stochastic modeling and simulation. However, they naturally have a limitation in mimicking actual user movements because it is impossible to capture all temporal, spatial, and behavioral characteristics that are implicitly melted in real user mobility. Along with the recent advancements of deep learning technologies, there have been several challenging proposals that exploit the power of the deep learning, especially generative models, in mobility synthesis. This paper reviews and summarizes recently proposed user mobility synthesis schemes based on generative adversarial networks that have been one of the most leading deep learning technologies for the last few years.
KSP 제안 키워드
Behavioral characteristics, Context-aware service, Development purposes, IT systems, Location-based applications, Mobility Traces, Mobility characteristics, Smart Mobility, Stochastic modeling and simulation, User Mobility, deep learning(DL)