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S. Homayouni, S. Schwarz, M. K. Müller, M. Rupp:
"CQI Mapping Optimization in Spatial Wireless Channel Prediction";
in: "2018 IEEE 87th Vehicular Technology Conference", S. Homayouni, R. Schwarz, M. K. Müller, M. Rupp (Hrg.); herausgegeben von: 2018 IEEE 87th Vehicular Technology Conference; IEEE Communications Society, Porto, 2018, S. 1 - 5.

In current wireless cellular networks, the BS tries to provide the highest possible rate to the users that can reliably be decoded. The BS obtains this rate information as a feedback from the users. However, the biased CQI reporting by the users impacts the rate and hence the system performance. The bias is directly related to the estimation error of the predicted SNR obtained by the Gaussian Process Regression (GPR) method in which the spatial correlation of the wireless channel is used for wireless channel prediction, and in particular, for predictive resource allocation. In this paper, the objective is to optimize the predicted SNR-CQI mapping by introducing an offset, adapted to the estimation accuracy relative to the user density. Results show that the proposed CQI mapping enhances the system performance for different user densities.

In current wireless cellular networks, the BS tries to provide the highest possible rate to the users that can reliably be decoded. The BS obtains this rate information as a feedback from the users. However, the biased CQI reporting by the users impacts the rate and hence the system performance. The bias is directly related to the estimation error of the predicted SNR obtained by the Gaussian Process Regression (GPR) method in which the spatial correlation of the wireless channel is used for wireless channel prediction, and in particular, for predictive resource allocation. In this paper, the objective is to optimize the predicted SNR-CQI mapping by introducing an offset, adapted to the estimation accuracy relative to the user density. Results show that the proposed CQI mapping enhances the system performance for different user densities.

channel estimation;optimisation;prediction theory;wireless channels;wireless cellular networks;Gaussian process regression method;predicted SNR-CQI mapping optimization;decoding;GPR method;channel quality indicator;predictive resource allocation;spatial c

https://ieeexplore.ieee.org/document/8417470