no code implementations • 8 Nov 2022 • Jun Gao, Dongze Wu, Feng Yin, Qinglei Kong, Lexi Xu, Shuguang Cui
The framework introduces two paradigms for the optimization of meta-parameters: a centralized paradigm that simplifies the process by sharing data from all historical environments, and a distributed paradigm that maintains data privacy by training meta-parameters for each specific environment separately.
no code implementations • 18 Mar 2021 • Kai Chen, Qinglei Kong, Yijue Dai, Yue Xu, Feng Yin, Lexi Xu, Shuguang Cui
Empowered by big data and machine learning, next-generation data-driven communication systems will be intelligent with the characteristics of expressiveness, scalability, interpretability, and especially uncertainty modeling, which can confidently involve diversified latent demands and personalized services in the foreseeable future.
no code implementations • 8 Mar 2020 • Feng Yin, Zhidi Lin, Yue Xu, Qinglei Kong, Deshi Li, Sergios Theodoridis, Shuguang, Cui
In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods.