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II. UAV Communications

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i. Cellular-Enabled UAV Communication

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1) Trajectory design under cellular connectivity constraint

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  • Seamless connectivity: We established a graph theory based framework to minimize the UAV's mission completion time subject to a cellular connectivity (in terms of SNR) constraint at any time instant during the flight. We proposed a polynomial-time algorithm that is proved to achieve arbitrarily close performance to that of the optimal solution.

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  • S. Zhang*, Y. Zeng, and R. Zhang, “Cellular-enabled UAV communication: A connectivity-constrained trajectory optimization perspective,” IEEE Trans. Commun., vol. 67, no. 3, pp. 2580-2604, Mar. 2019. (Editor-in-Chief Invited Paper, Top 10 Popular Article on TCOM from April to July 2019) (ESI Highly-Cited Paper) [IEEEXplore] [arXiv]

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  • S. Zhang, Y. Zeng, and R. Zhang, "Cellular-enabled UAV communication: Trajectory optimization under connectivity constraint," in Proc. IEEE Int. Conf. Commun. (ICC), May 2018.​​

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  • Connectivity with outage: To deal with the case where seamless connectivity cannot be achieved for the UAV (e.g., due to sparse BS distribution, high connectivity threshold), we devised a general cost function to measure the effect of outage durations during the UAV's flight. We then developed a general UAV trajectory optimization framework under outage cost constraints.​

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  • S. Zhang* and R. Zhang, "Trajectory optimization for cellular-connected UAV under outage duration constraint," J. Commun. Inf. Network., vol. 4, no. 4, pp. 55-71, Dec. 2019. (Invited Paper) [IEEEXplore] [arXiv]

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  • S. Zhang and R. Zhang, "Trajectory design for cellular-connected UAV under outage duration constraint," in Proc. IEEE Int. Conf. Commun. (ICC), May 2019.

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  • Trajectory optimization based on radio map: We established a radio map based approach to characterize the channel gain and interference power over a geographical region of interest, which captures the terrain features and thus provides more accurate channel/interference information compared to the model-based approaches. We developed a trajectory optimization framework based on the radio map, subject to the cellular connectivity constraint.  ​

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  • S. Zhang* and R. Zhang, "Radio map based 3D path planning for cellular-connected UAV," IEEE Trans. Wireless Commun., vol. 20, no. 3, pp. 1975-1989, Mar. 2021. [IEEEXplore] [arXiv]

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  • S. Zhang and R. Zhang, "Radio map based path planning for cellular-connected UAV," in Proc. IEEE Global Commun. Conf. (Globecom), Dec. 2019. [IEEEXplore] [arXiv]

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2) Aerial-Ground Interference Management

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  • L. Liu, S. Zhang*, and R. Zhang, "Multi-beam UAV communication in cellular uplink: Cooperative interference cancellation and sum-rate maximization," IEEE Trans. Wireless Commun., vol. 18, no. 10, pp. 4679-4691, Oct. 2019. [IEEEXplore] [arXiv]

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  • L. Liu, S. Zhang, and R. Zhang, "Exploiting NOMA for multi-beam UAV communication in cellular uplink," in Proc. IEEE Int. Conf. Commun. (ICC), May 2019. [arXiv]

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  • L. Liu, S. Zhang, and R. Zhang, "Cooperative interference cancellation for multi-beam uplink UAV communication: A DoF analysis," in Proc. IEEE Global Commun. Conf. (Globecom) Wkshps., Dec. 2018.

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ii. UAV-Assisted Cellular Communication

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  • L. Liu, S. Zhang*, and R. Zhang, "CoMP in the sky: UAV placement and movement optimization for multi-user communications," IEEE Trans. Commun., vol. 67, no. 8, pp. 5645-5658, Aug. 2019. [IEEEXplore] [arXiv]

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  • H. He, S. Zhang, Y. Zeng, and R. Zhang, “Joint altitude and beamwidth optimization for UAV-enabled multiuser communications,” IEEE Commun. Lett., vol. 22, no. 2, pp. 344-347, Feb. 2018. (ESI Highly-Cited Paper) [IEEEXplore] [arXiv]

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                                                                                               (*: corresponding author)

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