[Zurück]

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 (Hrg.); IEEE Communications Society, Chicago, IL, USA, USA, 2018, S. 1 - 5.

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.

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.

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

http://dx.doi.org/10.1109/VTCFall.2018.8690667

https://publik.tuwien.ac.at/files/publik_284566.pdf