Network Performance and Technological Feasibility of Unmanned Aerial Vehicles for Network Extension
DOI:
https://doi.org/10.31642/JoKMC/2018/110114%20Keywords:
UAV, Drones, Energy Conservation, Wireless, Cellular, 4G, LTEAbstract
The operational range of conventional and license-free radio-controlled drones is limited due to line-of-sight restrictions (LoS). There exists a definitive method for operating a drone. Consequently, in order to fly the drone beyond the visual line of sight (BVLoS), it is necessary to replace the drone's original wireless communications equipment with a device that requires a licence and is connected to a cellular network. Long-Term Evolution (LTE), a terrestrial communication technique, enables a drone to establish a real-time connection with a ground station. This connection serves the goals of command and control (C&C) as well as payload delivery. Nevertheless, it is important to note that the electromagnetic environment undergoes changes as altitude increases, which can potentially complicate the process of interfacing with drones over terrestrial cellular networks. The objective of this article is to develop a prototype control system for low-altitude microdrones using LTE technology. Additionally, it seeks to assess the feasibility and effectiveness of cellular connectivity for drones operating at various altitudes. This evaluation will be conducted by examining factors like as latency, handover, and signal strength. At a certain altitude, the received signal experiences a decrease in power level by 20 dBm and a degradation in signal quality by 10 dB. The data throughput of the downlink had a fall of 70%, while the latency exhibited an increase of 94 ms. Despite meeting the basic criteria for drone cellular connection, the existing LTE network necessitates enhancements in order to expand aerial coverage, mitigate interference, and minimise network latency.
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References
Zeng, Y.; Guvenc, I.; Zhang, R.; Geraci, G.; Matolak, D.W. UAV Communications for 5G and Beyond; Wiley Online Library: New York, NY, USA, 2020.
Wang, X.; Kealy, A.; Li, W.; Jelfs, B.; Gilliam, C.; May, S.L.; Moran, B. Toward Autonomous UAV Localization via Aerial Image Registration. Electronics 2021, 10, 435. [CrossRef]
Gupta, L.; Jain, R.; Vaszkun, G. Survey of essential issues in UAV communication networks. Ieee Commun. Surv. Tutor. 2015, 18, 1123–1152. [CrossRef]
Zeng, Y.; Zhang, R.; Lim, T.J. Wireless communications with uncrewed aerial vehicles: Opportunities and challenges. IEEE Commun. Mag. 2016, 54, 36–42. [CrossRef]
Mousavi, H.; Amiri, I.S.; Mostafavi, M.A.; Choon, C.Y. LTE physical layer: Performance analysis and evaluation. Appl. Comput. Inform. 2019, 15, 34–44. [CrossRef]
Zeng, Y.; Lyu, J.; Zhang, R. Cellular-connected UAV: Potential, challenges, and promising technologies. IEEE Wirel. Commun. 2018, 26, 120–127. [CrossRef]
Mitola, J. Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio, Doctor of Technology. Ph.D. Dissertation, Royal Institute of Technology, Stockholm, Sweden, 2000; pp. 271–350.
Reyes, H.; Gellerman, N.; Kaabouch, N. A Cognitive Radio System for Improving the Reliability and Security of UAS/UAV Networks. In Proceedings of the 2015 IEEE Aerospace Conference, Big Sky, MT, USA, 7–14 March 2015; pp. 1–9. [CrossRef]
Akpakwu, G.A.; Silva, B.J.; Hancke, G.P.; Abu-Mahfouz, A.M. A Survey on 5G Networks for the Internet of Things: Communication Technologies and Challenges. IEEE Access 2018, 6, 3619–3647. [CrossRef]
Saleem, Y.; Rehmani, M.H.; Zeadally, S. Integration of Cognitive Radio Technology with uncrewed aerial vehicles: Issues, opportunities, and future research challenges. J. Netw. Comput. Appl. 2015, 50, 15–31. [CrossRef]
Powell, K.; Abdalla, A.S.; Brennan, D.; Marojevic, V.; Barts, R.M.; Panicker, A.; Ozdemir, O.; Guvenc, I. Software Radios for Unmanned Aerial Systems. In Proceedings of the OpenWireless’20, 1st International Workshop on Open Software Defined Wireless Networks; Association for Computing Machinery: New York, NY, USA, 2020; pp. 14–20. [CrossRef]
Jacob, P.; Sirigina, R.P.; Madhukumar, A.S.; Prasad, V.A. Cognitive Radio for Aeronautical Communications: A Survey. IEEE Access 2016, 4, 3417–3443. [CrossRef]
“Ley 18/2014, de 15 de octubre, de aprobación de medidas urgentes para el crecimiento, la competitividad y la eficiencia,” Spanish Off. Bull., vol. 2014, no. 252, pp. 83921–84082, 2014.
L. Song and T. Huang, “A summary of key technologies of ad hoc networks with UAV node,” in International Conference on Intelligent Computing and Integrated Systems, 2010, pp. 944–949.
L. Gupta, R. Jain, and G. Vaszkun, “Survey of important issues in UAV communication networks,” IEEE Commun. Surv. Tutorials, vol. 18, no. 2, pp. 1123-1152, 2016.
Y. Saleem et al., “Integration of Cognitive Radio Technology with unmanned aerial vehicles: issues, opportunities, and future research challenges,” J. Netw. Comput. Appl., vol. 50, pp. 15–31, 2015.
J.D.M.M. Biomo, et al., “Routing in Unmanned Aerial Ad Hoc Networks: A Recovery Strategy for Greedy Geographic Forwarding Failure,” Proc. IEEE WCNC Mob. Wirel. Networks, pp. 2236–2241, 2014.
N. Uchida et al., “Proposal of Seeking Wireless Station by Flight Drones base don Delay Tolerant Networks,” Proc. 9th Int. Conference Broadband Wirel. Comput. Commun. Appl., pp. 401–405, 2014.
M. Bekhti et al., “Path Planning of Unmanned Aerial Vehicles with Terrestrial Wireless Network Planning,” Proc. Wirel. Days, pp. 1–6, 2016.
Intel, “BSP.” [Online]. Available: https://downloadcenter.intel.com/download/23197/IntelQuark-BSP. [Accessed: 06-Jul-2016].
H. T. Friis, “A note on a simple transmission formula,” in IRE’46, 1946, vol. 34 (5), pp. 254–256.
Winner and I. S. Technologies, “IST-4-027756 WINNER II. D1.1.2 V1.2. WINNER II Channel Models,” 2008.
A. Neumann et al., “Better Approach To Mobile Ad-hoc Networking (B.A.T.M.A.N.),” IETF Draft, 2008.
R. Sanchez-Iborra et al., “Performance evaluation of BATMAN routing protocol for VoIP services: a QoE perspective,” IEEE Trans. Wirel. Commun., vol. 13, no. 9, pp. 4947 – 4958, 2014.
Valavanis, K.P.; Vachtsevanos, G.J. (Eds.) Handbook of Unmanned Aerial Vehicles; Springer: Dordrecht, The Netherlands, 2015; p. 3022. [CrossRef]
Saleem, Y.; Rehmani, M.H.; Zeadally, S. Integration of Cognitive Radio Technology with uncrewed aerial vehicles: Issues, opportunities, and future research challenges. J. Netw. Comput. Appl. 2015, 50, 15–31. [CrossRef]
Bento, M.D.F. (Ed.) Uncrewed Aerial Vehicles: An Overview; Inside GNSS: Hoboken, NJ, USA, 2008; pp. 54–61.
Cheng, C., Adulyasak, Y. & Rousseau, L.-M. (2020), ‘Drone routing with energy function: Formulation and exact algorithm,’ Transportation Research Part B: Methodological 139, 364–387.
Tennekes, H. (2009), The Simple Science of Flight, Revised and Expanded Edition: From Insects to Jumbo Jets, MIT press.
Thibbotuwawa, A., Nielsen, P., Zbigniew, B. & Bocewicz, G. (2018a), Energy consumption in uncrewed aerial vehicles: A review of energy consumption models and their relation to the UAV routing, in ‘International Conference on Information Systems Architecture and Technology,’ Springer, pp. 173–184.
Thibbotuwawa, A., Nielsen, P., Zbigniew, B. & Bocewicz, G. (2018b), Factors affecting energy consumption of uncrewed aerial vehicles: an analysis of how energy consumption changes about UAV routing, in ‘International Conference on Information Systems Architecture and Technology,’ Springer, pp. 228–238.
Demir, E., Bekta¸s, T. & Laporte, G. (2014), ‘A review of recent research on green road freight transportation,’ European journal of operational research 237(3), 775–793.
Zhang, J., Campbell, J. F., Sweeney II, D. C. & Hupman, A. C. (2021), ‘Energy consumption models for delivery drones: A comparison and assessment,’ Transportation Research Part D: Transport and Environment 90, 102668.
Famili, A., Stavrou, A., Wang, H. et al. (2022), ‘Optilod: Optimal beacon placement for high-accuracy indoor localization of drones,’ arXiv preprint arXiv:2201.10691.
Ng, K.J.; Islam, M.T.; Alevy, A.; Mansor, M.F.; Su, C.C. Azimuth Null-Reduced Radiation Pattern, Ultralow Profile, DualWideband and Low Passive Intermodulation Ceiling Mount Antenna for Long Term Evolution Application. IEEE Access 2019, 7, 114761–114777. [CrossRef]
Monem, M.A.; Netmanias. Why No Soft Handover in LTE? 2016. Available online: https://www.netmanias.com/en/post/blog/ 11023/handover-lte/why-no-soft-handover-in-lte (accessed on 9 March 2021).
Muruganathan, S.D.; Lin, X.; Maattanen, H.-L.; Sedin, J.; Zou, Z.; Hapsari, W.A.; Yasukawa, S. An overview of 3GPP release-15 study on enhanced LTE support for connected drones. arXiv 2018, arXiv:1805.00826.
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