2 citations · 2 across the 2 of their papers we have counts for
7 papers
Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey
Kai Chen, Qinglei Kong, Yijue Dai +4
Data-driven paradigms are well-known and salient demands of future wireless communication. Empowered by big data and machine learning, next-generation data-driven communication sys…
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing
Feng Yin, Zhidi Lin, Yue Xu +5
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 da…
Scalable Learning Paradigms for Data-Driven Wireless Communication
Yue Xu, Feng Yin, Wenjun Xu +3
The marriage of wireless big data and machine learning techniques revolutionizes the wireless system by the data-driven philosophy. However, the ever exploding data volume and mode…
A General Hyper-Parameter Optimization for Gaussian Process Regression with Cross-Validation and Non-linearly Constrained ADMM
Linning Xu, Feng Yin, Jiawei Zhang +2
Hyper-parameter optimization remains as the core issue of Gaussian process (GP) for machine learning nowadays. The benchmark method using maximum likelihood (ML) estimation and gra…
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification
Yue Xu, Feng Yin, Wenjun Xu +2
The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, wireless traffic predict…
Multi-Antenna Channel Interpolation via Tucker Decomposed Extreme Learning Machine
Han Zhang, Bo Ai, Wenjun Xu +2
Channel interpolation is an essential technique for providing high-accuracy estimation of the channel state information (CSI) for wireless systems design where the frequency-space…