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Contributions to Proceedings:

S. Homayouni, S. Schwarz, M. Rupp:
"On CQI Estimation for Mobility and Correlation Properties of Gaussian Process Regression";
in: "2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)", S. Homayouni, S. Schwarz, M. Rupp (ed.); IEEE Communications Society, Chicago, IL, USA, USA, 2019, 1 - 5.



English abstract:
Channel quality prediction is an essential function for anticipatory and proactive radio resource allocation. In this paper, we propose a channel quality prediction method based on the concept of Gaussian Process Regression (GPR) in which the spatio-temporal correlation of the wireless channel is used for wireless channel prediction. The objective of the paper is to find the optimal channel quality prediction for non-static users. Furthermore, we propose our analytical optimization in the choice of users which enhances the spatio-temporal correlation of the wireless channel and results in performance improvements in terms of BLER and rate loss. Simulation results show the potential of our proposed method.

German abstract:
Channel quality prediction is an essential function for anticipatory and proactive radio resource allocation. In this paper, we propose a channel quality prediction method based on the concept of Gaussian Process Regression (GPR) in which the spatio-temporal correlation of the wireless channel is used for wireless channel prediction. The objective of the paper is to find the optimal channel quality prediction for non-static users. Furthermore, we propose our analytical optimization in the choice of users which enhances the spatio-temporal correlation of the wireless channel and results in performance improvements in terms of BLER and rate loss. Simulation results show the potential of our proposed method.

Keywords:
5G, channel state information, mobility, optimization, Gaussian process regression


"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/VTCFall.2018.8690667

Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_284566.pdf


Created from the Publication Database of the Vienna University of Technology.