Search Results for author: Qinglei Kong

Found 3 papers, 0 papers with code

MetaLoc: Learning to Learn Wireless Localization

no code implementations8 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.

Meta-Learning

Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey

no code implementations18 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.

BIG-bench Machine Learning Gaussian Processes

FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

no code implementations8 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.

Federated Learning

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