Channel Beam Pattern Extension for Massive MIMO via Deep Gaussian Process Regression

Zhang, Cheng; Wang, Mengzhe; He, Weiliang; Zhang, Lujia; Huang, Yongming

doi:10.1109/iccc52777.2021.9580358

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, recent studies estimate or directly use the MU location or geometric environment between transceivers as side information for beam prediction [25,35]- [38]. The Gaussian process (GP) is a probabilistic machine learning model that performs inference with uncertainties, and it has been well-applied in small-sample low-dimensional beam prediction [25,37]- [39]. In mmWave fixed wireless access, researchers develop an explicit mapping between transmit/receive beams and MU physical coordinates via a GP [37].…”

Section: A Related Workmentioning

confidence: 99%

Evaluation of Private Enterprises Based on Deep Learning

Meng

Zhang

2022

Mobile Information Systems

View full text Add to dashboard Cite

In this paper, we conduct an in-depth research on the corresponding enterprises, combined with some problems existing in the process of data processing and use. We establish a deep learning model on the extensive collection and comprehensive investigation of the research results of domestic and foreign enterprises in all aspects of the process of data processing and use, and determine the research directions. Firstly, in view of the increasing complexity and dimension of enterprise data, and the difficulties of enterprise data application, this paper studies the related data preprocessing methods. Secondly, aiming at the problems of enterprise cost control and customer relationship management, this paper studies the prediction based on enterprise data through the analysis of practical problems and the processing of corresponding data. Finally, in order to progress and advance the efficiency and scientific usefulness of enterprise management, we in this paper study the evaluation based on enterprise data. The model is verified through simulations and compared with several models i.e. cross hybrid and sequential hybrid models. Using certain assumptions, the attained outcomes confirm that the accuracy of the deep learning structure of the single model is sophisticated and greater than that of the cross hybrid model, but lower than that of the sequential hybrid model.

show abstract