Scenario-6: Passive Fixed Active Mobile (PFAM)

In this experimental setup, an AERPAW owned drone which is operated by a designated pilot for safety purposes will carry a PAW, such as an SDR. The goal of the drone is to sense the signals radiated from nearby RF sources not owned by AERPAW. The drone can also be allowed to carry radar (or the SDRs may be used as a radar) for capturing the signals reflected from the environment. This experiment can play a critical role in ensuring reliable signal coverage from terrestrial networks at the drone’s end, which, in turn, can materialize fully autonomous drone integration into next-generation cellular networks. Similar to Scenario 3, we will have a tether option for the drones which will ensure longer operation. An example of the following scenario is illustrated in the adjacent figure.

Experiments in this scenario can use drones to capture signals from existing 4G/5G base stations at different drone heights, antenna tilts, transmission frequencies, etc., and characterize the coverage. Similar experiments were also reported e.g. by Qualcomm and Ericsson. Such data can be used to optimize coverage of commercial base stations for better serving drones. Several related papers in the literature on related experiments are highlighted below.

Another class of PFAM experiments is passive source localization and tracking. For example, drones may capture probe requests signals from WiFi, or other cellular signals, for identifying a victim after a disaster, a jammer on the ground, or a hostile drone. For example, one of AERPAW’s municipality partners used a helicopter to be able to identify a jammer signal on one of its public safety bands: it has not been possible to triangulate the jammer from the ground, but as soon as sensing is performed high above the ground, it was possible to triangulate and identify the interference source quickly.

LTE Cellular Coverage

[1] A. Al-Hourani, S. Kandeepan, and A. Jamalipour, “Modeling air-to-ground path loss for low altitude platforms in urban environments,” in Proc. IEEE Globecom, Austin, TX, Dec 2014, pp. 2898–2904.
[2] M. Batistatos, “LTE ground-to-air measurements for UAV-assisted cellular networks,” IET Conf. Proc., pp. 801 (5 pp.)–801 (5 pp.)(1), Jan. 2018.
[3] S. Euler, H. Maattanen, X. Lin, Z. Zou, M. Bergstrom, and J. Sedin, “Mobility support for cellular-connected unmanned aerial vehicles: Performance and analysis,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Apr. 2019, pp. 1–6.
[4] H. C. Nguyen, R. Amorim, J. Wigard, I. Z. Kovacs, T. B. Sørensen, and P. E. Mogensen, “How to ensure reliable connectivity for aerial vehicles over cellular networks,” IEEE Access, vol. 6, pp. 12 304–12 317, 2018.
[5] J. Stanczak, I. Z. Kovacs, D. Koziol, J. Wigard, R. Amorim, and H. Nguyen, “Mobility challenges for unmanned aerial vehicles connected to cellular LTE networks,” in IEEE Veh. Technol. Conf. (VTC), June 2018, pp. 1–5.
[6] T. Izydorczyk, M. M. Ginard, S. Svendsen, G. Berardinelli, and P. Mogensen, “Experimental evaluation of beamforming on UAVs in cellular systems,” 2020.
[7] R. Amorim, H. Nguyen, P. Mogensen, I. Z. Kovacs, J. Wigard, and T. B. Sørensen, “Radio channel modeling for UAV communication over cellular networks,” IEEE Wireless Commun. Lett., vol. 6, no. 4, pp. 514– 517, 2017.
[8] Qualcomm, “LTE Unmanned Aircraft Systems,” May 2017. [Online]. Available:
[9] I. Kovacs, R. Amorim, H. C. Nguyen, J. Wigard, and P. Mogensen, “Interference analysis for UAV connectivity over LTE using aerial radio measurements,” in Proc. IEEE Veh. Technol. Conf. (VTC), 2017, pp. 1–6.
[10] R. Amorim, P. Mogensen, T. Sorensen, I. Z. Kovacs, and J. Wigard, “Pathloss measurements and modeling for UAVs connected to cellular networks,” in Proc. IEEE Veh. Technol. Conf., 2017, pp. 1–6.
[11] B. Van Der Bergh, A. Chiumento, and S. Pollin, “LTE in the sky: trading off propagation benefits with interference costs for aerial nodes,” IEEE Commun. Mag., vol. 54, no. 5, pp. 44–50, 2016.
[12] G. E. Athanasiadou, M. C. Batistatos, D. A. Zarbouti, and G. V. Tsoulos, “LTE ground-to-air field measurements in the context of flying relays,” IEEE Wireless Commun., vol. 26, no. 1, pp. 12–17, 2019.

5G Cellular Coverage

[1] R. Muzaffar, C. Raffelsberger, A. Fakhreddine, J. L. Luque, D. Emini, and C. Bettstetter, “First experiments with a 5G-connected drone,” in Proc. ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, ser. DroNet ’20. New York, NY, USA: Association for Computing Machinery, 2020. [Online]. Available:
[2] D. Mishra and E. Natalizio, “A survey on cellular-connected UAVs: Design challenges, enabling 5G/B5G innovations, and experimental advancements,” ArXiv, vol. abs/2005.00781, 2020.
[3] V. Platzgummer, V. Raida, G. Krainz, P. Svoboda, M. Lerch, and M. Rupp, “UAV-based coverage measurement method for 5G,” in Proc. IEEE Veh. Technol. Conf., 2019, pp. 1–6.
[4] B. Sadhu, A. Paidimarri, M. Ferriss, M. Yeck, X. Gu, and A. Valdes-Garcia, “A software-defined phased array radio with mmWave to software vertical stack integration for 5G experimentation,” in Proc. IEEE/MTT-S Int. Microwave Symp. – IMS, 2018, pp. 1323–1326.

Passive Source Localization/ Tracking

[1] V. Acuna, A. Kumbhar, E. Vattapparamban, F. Rajabli, and I. Guvenc, “Localization of WiFi devices using probe requests captured at unmanned aerial vehicles,” in Proc. Wireless Commun. Netw. Conf. (WCNC), 2017, pp. 1–6.
[2] M. M. U. Chowdhury, F. Erden, and I. Guvenc, “RSS based Q-learning for indoor UAV navigation,” in Proc. IEEE Military Commun. Conf. (MILCOM), 2019, pp. 121–126.
[3] F. Koohifar, I. Guvenc, and M. L. Sichitiu, “Autonomous tracking of intermittent RFsource using a UAV swarm,” IEEE Access, vol. 6, pp. 15 884–15 897, 2018.
[4] F. Koohifar, A. Kumbhar, and I. Guvenc, “Receding horizon multi-UAV cooperative tracking of moving RF source,” IEEE Commun. Lett., vol. 21, no. 6, pp. 1433– 1436, 2017.
[5] I. Guvenc, F. Koohifar, S. Singh, M. L. Sichitiu, and D. Matolak, “Detection, tracking, and interdiction for amateur drones,” IEEE Commun. Mag., vol. 56, no. 4, pp. 75–81, 2018.
[6] L. Dressel and M. J. Kochenderfer, “Hunting drones with other drones: Tracking a moving radio target,” in Int. Conf. on Robotics and Automation (ICRA), 2019, pp. 1905–1912.