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Contributions to Proceedings:

S. Homayouni, S. Schwarz, M. Rupp:
"Impact of SIR Estimation on Feedback Reduction During Heavy Crowd Events in 4G/5G Networks";
in: "2019 International Conference on Systems, Signals and Image Processing (IWSSIP)", IEEE Communications Society, Osijek, Croatia, Croatia, 2019, 1 - 5.



English abstract:
Crowd events such as large-scale cultural entertainment events, urban festivals, sport events, in particular, football world cups, cause a high traffic load to the mobile networks, because a large number of users compete simultaneously to access the radio resources. During such heavy events, the uplink traffic is high and hence the network becomes uplink-limited, in which the Channel Quality Indicator (CQI) feedback received from the users to the Base Station (BS) consumes much of the uplink resources. In practical scenarios, CQI feedback strategies should be able to reduce the signalling overhead while satisfying certain performance bounds of Block Error Ratio (BLER), as an indicator of the connection quality. In this paper, we aim to reduce the feedback overhead in a multi-BS scenario. We cast the problem of Signal-to-Interference Ratio (SIR) estimation by exploiting the theory of Gaussian Process Regression (GPR), which takes the advantage of macroscopic shadow fading correlation properties. Our results demonstrate that with GPR and with the consideration of the interfering BS, an efficient reduction in feedback is achieved.

German abstract:
Crowd events such as large-scale cultural entertainment events, urban festivals, sport events, in particular, football world cups, cause a high traffic load to the mobile networks, because a large number of users compete simultaneously to access the radio resources. During such heavy events, the uplink traffic is high and hence the network becomes uplink-limited, in which the Channel Quality Indicator (CQI) feedback received from the users to the Base Station (BS) consumes much of the uplink resources. In practical scenarios, CQI feedback strategies should be able to reduce the signalling overhead while satisfying certain performance bounds of Block Error Ratio (BLER), as an indicator of the connection quality. In this paper, we aim to reduce the feedback overhead in a multi-BS scenario. We cast the problem of Signal-to-Interference Ratio (SIR) estimation by exploiting the theory of Gaussian Process Regression (GPR), which takes the advantage of macroscopic shadow fading correlation properties. Our results demonstrate that with GPR and with the consideration of the interfering BS, an efficient reduction in feedback is achieved.

Keywords:
5G, channel quality indication, feedback overheadreduction, Gaussian process regression, interference, SIR


"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/IWSSIP.2019.8787330

Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_284568.pdf


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