Papers by Ursula Challita
Due to the dramatic growth in mobile data traffic on one hand and the scarcity of the licensed sp... 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 under the name of LTE Licensed-Assisted Access (LTE-LAA). In this paper, we take a holistic approach for LTE-LAA small cell traffic balancing by jointly optimizing the use of licensed and unlicensed bands. We pose this traffic balancing as an optimization problem that seeks proportional fair coexistence of WiFi, small cell and macro cell users by adapting the transmission probability of the LTE-LAA small cell in the licensed and unlicensed bands. The motivation for this formulation is for the LTE-LAA small cell to switch between or aggregate licensed and unlicensed bands depending on the interference/traffic level and number of active users in each band. We derive a closed form solution for this optimization problem and additionally propose a transmission mechanism for the operation of the LTE-LAA small cell on both bands. Through numerical and simulation results , we show that our proposed traffic balancing scheme, besides enabling better LTE-WiFi coexistence, leads to an efficient utilization of the radio resources compared to alternative approaches as it provides a better tradeoff between maximizing the total network throughput and achieving fairness among all network flows.
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Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing - MobiHoc '16, 2016
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MELECON 2014 - 2014 17th IEEE Mediterranean Electrotechnical Conference, 2014
ABSTRACT In this paper, we consider an LTE downlink Multiple Input Multiple Output (MIMO) Coordin... more ABSTRACT In this paper, we consider an LTE downlink Multiple Input Multiple Output (MIMO) Coordinated Multipoint (CoMP) network. We propose a new adaptive precoding method applied on the signals being transmitted by the set of active antennas. Based on Joint Processing (JP) technique and the precoding method, we define a joint cellular area of cooperation between different evolved NodeBs (eNodeBs) serving a given user equipment (UE). The set of active antennas is selected based on the channel information at the transmitters. Simulations are performed in terms of throughput and Bit Error Rate (BER) for the cell edge users. Results show that an optimized number of the active antennas exists. Moreover, results show that additional increase of this number has a limited throughput and BER improvement. The latter comes at the detriment of additional backhaul loading and reduced overall user capacity.
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Drafts by Ursula Challita
LTE in unlicensed spectrum (LTE-U) is a promising approach to overcome the wireless spectrum scar... more 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 (SBSs). The proposed approach enables multiple SBSs to proactively perform dynamic channel selection, carrier aggregation, and fractional spectrum access while guaranteeing fairness with existing WiFi networks and other LTE-U operators. Adopting a proactive coexistence mechanism enables future delay-intolerant LTE-U data demands to be served within a given prediction window ahead of their actual arrival time thus avoiding the underutilization of the unlicensed spectrum during off-peak hours while maximizing the total served LTE-U traffic load. To this end, a noncooperative game model is formulated in which SBSs are modeled as Homo Egualis agents that aim at predicting a sequence of future actions and thus achieving long-term equal weighted fairness with WLAN and other LTE-U operators over a given time horizon. The proposed deep learning algorithm is then shown to reach a mixed-strategy Nash equilibrium (NE), when it converges. Simulation results using real data traces show that the proposed scheme can yield up to 28% and 11% gains over a conventional reactive approach and a proportional fair coexistence mechanism, respectively. The results also show that the proposed framework prevents WiFi performance degradation for a densely deployed LTE-U network.
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Papers by Ursula Challita
Drafts by Ursula Challita