Ursula Challita

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LTE in unlicensed spectrum (LTE-U) is a promising approach to overcome the wireless spectrum scarcity. However, to reap the benefits of LTE-U, a fair coexistence mechanism with other incumbent WiFi deployments is required. In this paper, a novel deep learning approach is proposed for modeling the resource allocation problem of LTE-U small base stations(More)
The coexistence of LTE-Unlicensed (LTE-U) and WiFi in unlicensed spectrum is studied in the context of airtime sharing. We consider core problem where a set of LTE-U cells from different operators share the same channel as a co-located WiFi access point (AP). We assume that LTE-U cells utilize Listen-Before-Talk (LBT) as the default channel access(More)
Due to the dramatic growth in mobile data traffic on one hand and the scarcity of the licensed spectrum on the other hand, mobile operators are considering the use of unlicensed bands (especially those in 5 GHz) as complementary spectrum for providing higher system capacity and better user experience. This approach is currently being standardized by 3GPP(More)
To reap the benefits of dense small base station (SBS) deployment, innovative backhaul solutions are needed in order to manage scenarios in which high-speed ground backhaul links are either unavailable or limited in capacity. In this paper, a novel backhaul scheme that utilizes unmanned aerial vehicles (UAVs) as an on-demand flying network linking ground(More)
In mobile networks, traffic fluctuates heavily over time which rises uncertainty in the cell load for an eNodeB. Conventional radio network planning (RNP) focuses on a static model for the traffic distribution which is usually taken at hours of peak demand. However, a major disadvantage of such a deterministic model is that the locations of the eNodeBs are(More)
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