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S. Homayouni, S. Schwarz, M. K. Müller, M. Rupp:
"Reducing CQI Feedback Overhead by Exploiting Spatial Correlation";
in: "2018 IEEE 87th Vehicular Technology Conference", herausgegeben von: 2018 IEEE 87th Vehicular Technology Conference; IEEE Communications Society, 2018, S. 1 - 5.

Spatial wireless channel prediction is crucial for future wireless networks, and in particular, for predictive resource allocation. In this paper, we first predict the channel quality indicator (CQI) at an arbitrary test user based on the Gaussian process regression (GPR) method. Second, in order to limit the overall signalling overhead, we exploit the correlation property of the wireless propagation channel. The performance of the proposed method is evaluated by the Cram'er- Rao bound (CRB). Simulation results not only well demonstrate the potential of our proposed method, but also match with the theoretical analysis.

Spatial wireless channel prediction is crucial for future wireless networks, and in particular, for predictive resource allocation. In this paper, we first predict the channel quality indicator (CQI) at an arbitrary test user based on the Gaussian process regression (GPR) method. Second, in order to limit the overall signalling overhead, we exploit the correlation property of the wireless propagation channel. The performance of the proposed method is evaluated by the Cram'er- Rao bound (CRB). Simulation results not only well demonstrate the potential of our proposed method, but also match with the theoretical analysis.

5G, channel state information, signalling overhead, Gaussian process regression.

http://dx.doi.org/10.1109/VTCSpring.2018.8417549

https://ieeexplore.ieee.org/document/8417549