collection from ground‐based sensors and caching scenarios. UAV trajectory planning is mostly effected by the dimension of the target area, flight duration of the mission, QoS requirement by the ground users, and energy constraints. Apart from physical parameters, UAV trajectory optimization is analytically a challenging problem because it involves a fixed number of optimization variables related to the UAV locations [1]. In addition, UAV trajectory optimization requires coupling between different QoS metrics in wireless communication with the mobility of UAV. Recently, there have been a number of studies on the joint trajectory optimization of UAV with its wireless communication metrics, such as throughput maximization in [50–52] and energy‐efficient UAV communication in [53,54].
2.5.3 Energy Efficiency and Resource Management
Energy efficiency and resource management require attention where UAVs are operating in key scenarios to collect data from IoT devices, ensure public safety, and support cellular wireless network. Resource management is a major challenge in UAV communications unlike in cellular communications [55]. However, UAV communications introduce additional hindrance in radio resource management due to the interplay between the UAV flight duration, mobility pattern, limited energy source, and spectral efficiency. Therefore, in [56], resource management was jointly optimized with the UAV trajectory in wireless environment.
Limited amount of on‐board energy is available for battery‐operated UAV, which must be used for propulsion and to fulfill communication‐related tasks [5]. Consequently, continuous and long‐term wireless coverage curtails the UAV flight time. In addition, UAV energy consumption also depends on its path, weather condition, and mission of the UAV. Thus, energy constraints of UAV must be explicitly taken into account during planning of the UAV‐based communication systems. Various works have studied the interplay between energy efficiency and the optimal UAV trajectory [53–55].
2.6 Conclusion
This chapter discussed the use of UAVs in wireless communication network, specifically, the use of UAVs as aerial BSs and as aerial UE in cellular‐assisted systems. In both cases, the accurate channel model of the AG and AA propagation is paramount, which must take into account the environmental conditions, wireless channel impairments, and the UAV mobility to characterize the performance of UAV‐based communication network. Some channel modeling efforts have been studied in this chapter. In addition, key challenges, such as optimal deployment of UAVs, optimization of trajectory path, resource management, and energy efficiency, have also been highlighted.
Bibliography
1 1 Zeng, Y., Zhang, R., and Lim, T.J. (2016). Wireless communications with unmanned aerial vehicles: opportunities and challenges. IEEE Communications Magazine 54 (5): 36–42.
2 2 Qualcomm Technologies Inc. (2016). Leading the World to 5G: Evolving Cellular Technologies for Safer Drone Operation. Technical report. Qualcomm.
3 3 Patterson, T. (2015). Google, Facebook, SpaceX, OneWeb plan to beam internet everywhere. https://edition.cnn.com/2015/10/30/tech/pioneers-google-facebook-spacex-oneweb-satellite-drone-balloon-internet/index.html (accessed 08 March 2021).
4 4 Al‐Hourani, A., Kandeepan, S., and Jamalipour, A. (2014). Modeling air‐to‐ground path loss for low altitude platforms in urban environments. 2014 IEEE Global Communications Conference, pp. 2898–2904.
5 5 Fotouhi, A., Qiang, H., Ding, M. et al. (2019). Survey on UAV cellular communications: practical aspects, standardization advancements, regulation, and security challenges. IEEE Communications Surveys Tutorials 21 (4): 3417–3442.
6 6 Molisch, A. (2011). Wireless Communications. Wiley ‐ IEEE.
7 7 3GPP (2017). Study on Enhanced LTE Support for Aerial Vehicles. Technical report, 3rd Generation Partnership Project 3GPP.
8 8 Yanmaz, E., Kuschnig, R., and Bettstetter, C. (2011). Channel measurements over 802.11a‐based UAV‐to‐ground links. 2011 IEEE GLOBECOM Workshops (GC Wkshps), pp. 1280–1284.
9 9 Yanmaz, E., Kuschnig, R., and Bettstetter, C. (2013). Achieving air‐ground communications in 802.11 networks with three‐dimensional aerial mobility. 2013 Proceedings IEEE INFOCOM, pp. 120–124.
10 10 Ahmed, N., Kanhere, S.S., and Jha, S. (2016). On the importance of link characterization for aerial wireless sensor networks. IEEE Communications Magazine 54 (5): 52–57.
11 11 Khawaja, W., Guvenc, I., and Matolak, D. (2016). UWB channel sounding and modeling for UAV air‐to‐ground propagation channels. 2016 IEEE Global Communications Conference (GLOBECOM), pp. 1–7.
12 12 Newhall, W.G., Mostafa, R., Dietrich, C. et al. (2003). Wideband air‐to‐ground radio channel measurements using an antenna array at 2 GHz for low‐altitude operations. IEEE Military Communications Conference, 2003. MILCOM 2003, Volume 2, pp. 1422–1427.
13 13 Tu, H.D. and Shimamoto, S. (2009). A proposal of wide‐band air‐to‐ground communication at airports employing 5‐GHz band. 2009 IEEE Wireless Communications and Networking Conference, pp. 1–6.
14 14 Matolak, D.W. and Sun, R. (2017). Air‐ground channel characterization for unmanned aircraft systems‐part III: the suburban and near‐urban environments. IEEE Transactions on Vehicular Technology 66 (8): 6607–6618.
15 15 Sun, R. and Matolak, D.W. (2017). Air‐ground channel characterization for unmanned aircraft systems part II: Hilly and mountainous settings. IEEE Transactions on Vehicular Technology 66 (3): 1913–1925.
16 16 Meng, Y.S. and Lee, Y.H. (2011). Measurements and characterizations of air‐to‐ground channel over sea surface at C‐band with low airborne altitudes. IEEE Transactions on Vehicular Technology 60 (4): 1943–1948.
17 17 Al‐Hourani, A., Kandeepan, S., and Lardner, S. (2014). Optimal lap altitude for maximum coverage. IEEE Wireless Communications Letters 3 (6): 569–572.
18 18 Bor‐Yaliniz, R.I., El‐Keyi, A., and Yanikomeroglu, H. (2016). Efficient 3‐D placement of an aerial base station in next generation cellular networks. 2016 IEEE International Conference on Communications (ICC), pp. 1–5.
19 19 Bor‐Yaliniz, I. and Yanikomeroglu, H. (2016). The new frontier in ran heterogeneity: multi‐tier drone‐cells. IEEE Communications Magazine 54 (11): 48–55.
20 20 Hayajneh, A.M., Zaidi, S.A.R., McLernon, D.C., and Ghogho, M. (2016). Drone empowered small cellular disaster recovery networks for resilient smart cities. 2016 IEEE International Conference on Sensing, Communication and Networking (SECON Workshops), pp. 1–6.
21 21 Gomez, K., Hourani, A., Goratti, L. et al. (2015). Capacity evaluation of aerial LTE base‐stations for public safety communications. 2015 European Conference on Networks and Communications (EuCNC), pp. 133–138.
22 22 Chen, M., Mozaffari, M., Saad, W. et al. (2017). Caching in the sky: proactive deployment of cache‐enabled unmanned aerial vehicles for optimized quality‐of‐experience. IEEE Journal on Selected Areas in Communications 35 (5): 1046–1061.
23 23 Challita, U. and Saad, W. (2017). Network formation in the sky: unmanned aerial vehicles for multi‐hop wireless backhauling. GLOBECOM 2017 ‐ 2017 IEEE Global Communications Conference, pp. 1–6.
24 24 Kalantari, E., Yanikomeroglu, H., and Yongacoglu, A. (2016). On the number and 3D placement of drone base
