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systems. At mmWave frequencies more efficient beam training and tracking methods, which can handle rapid variations of path gain and fast deviations in angle of arrival and departure of beam, are required. Strategies to detect the abrupt changes and designing of smart precoders are required to improve the performance of UAV assisted mmWave communication systems. In UAV to BS mmWave communication, UAV detection and positioning is another critical challenge to be sorted out. In order to solve the interference problems, beam width needs to be designed judiciously. Higher frequencies will give narrow beams but result in heavy training overheads for beam alignment as in UAV-to-BS mmWave communication. On other hand, broader beams will increase interference to other cells. Efficient spectrum sharing schemes for increasing the network throughput and spectral efficiency needs to be designed for UAV mmWave communication.

      2.2.4 Deployment of UAV

Another challenging issue in UAV based communication networks is the deployment of the UAVs. Parameters like the geographical area, location of ground users, altitude of the UAV, etc. play a critical role in determining the performance of UAV-based communications Simultaneous deployment of UAVs causes inter system interference. Both distance and Line of sight probability are to be considered while deciding the optimal altitude. Deployment at lower altitudes poses problems of lower coverage and less probabilities of LoS links due to the shadowing effect whereas UAVs at higher altitudes tend to exhibit poor coverage performances on account of higher path losses because of the large distance between transmitter and receiver [51]. Research work of Refs. [15, 16, 20, 52] discusses the algorithms developed to find the optimal placement of LAP’s, the maximum number of UAVs required to serve all the users on the ground, the impact of UAV’s altitude on the performance of networks, placement of UAV’s for maximizing the coverage etc. Optimal deployment of UAVs reduces the average transmit power of the devices in an UAV-IoT communication network [53]. Also, the number of UAV’s required in such networks is also determined by the altitude of the UAVs [33]. UAVs with directional antennas and higher antenna beam widths perform well even if deployed at lower altitudes. Another challenge lies in determining the continuous UAV trajectory as it involves a large number of variables. A solution to this has been discussed in Ref. [54].

      2.2.5 Trajectory Optimization

      The performance of UAV assisted wireless networks can be significantly improved in aspect of throughput as well as coverage by optimizing the trajectory of the UAVs. This optimization depends upon the factors like flight constraints, energy constraints, ground user’s demands, collision avoidance, channel variations, mobility of UAV, etc. Table 2.2 lists the work carried out till date for optimizing the performance of the UAV systems by designing the optimum UAV trajectory.

      The article by Chen et al. [62] proposes autonomous UAV wherein positions of the UAVs are self-optimized based on real time radio measurement.

      Table 2.2 State-of-the-art solutions for optimizing the UAV trajectory.

Parameter optimized	Effect on performance of system	Research article
User scheduling and trajectory of UAV	Maximized the minimum average data rate experienced by ground users	[55]
Trajectory of UAV with multiple antennas	Maximized system rate in uplink communication	[56]
Joint optimization of UAV trajectory and source/relay transmit power	Maximized throughput of relay based UAV system	[57]
Path planning algorithm	Minimized total energy consumption of the UAV	[53, 58]
UAV trajectory using mixed integer linear programming	Fuel consumption minimization	[59]
Path planning	Likelihood of target detection	[60]
Trajectory of UAV	Connecting of ad-hoc networks was improved	[61]

      2.2.6 On-Board Energy

      Another major factor having a crucial impact on the performance of UAV assisted wireless communication networks is the limited available UAV on-board energy. This in turn limits the UAV flight and hovering duration. Over a period of time, research work has been carried out as in Refs. [63–74], where various methods have been proposed for minimizing the energy usage of UAVs in UAV communication. Few solutions proposed to encounter this challenge can be listed as, UAV optimal trajectory determining, efficient scheduling in multiple UAV scenario, dynamically activating only the required number of drones at a particular time, optimization of transmission times, reducing the required transmit power, efficient resource allocation schemes, energy harvesting for operations of small UAVs and many more. Managing the available resources of energy, bandwidth and time plays a crucial role in improving the performance of UAV communication systems [70, 75].

2.3 Conclusion

      UAV aided wireless communication networks is yet another important step towards the development of future smart cities of 5G-IoT era. For the past 4 decades UAVs have been occupying the sky and playing a vital role in wireless communication systems. Researchers across the globe have identified various challenges of this technology and have proposed feasible solutions to these problems. Efforts have been made here to highlight few of the challenges to be overcome while designing the optimum UAV-assisted networks, thereby paving a path for the budding researchers to tread upon. Over the past few years many research challenges have been identified and worked upon and this technology is being updated at a tremendous speed. The progress is still ongoing.

References

      1. Singhal, G., Bansod, B., Mathew, L., Unmanned Aerial Vehicle Classification, Applications and Challenges: A Review, 2018.

      2. Shakhatreh, H. et al., Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges. IEEE Access, 7, 48572–48634, 2019.

      3. Khawaja, W., Ozdemir, O., Guvenc, I., UAV air-to-ground channel characterization for mmWave systems. Proc. IEEE VTC Fall Workshops, Sep. 2017.

      4. NTT DOCOMO Inc. and Ericsson, New SID on Study on enhanced LTE Support for Aerial Vehicles, 3GPP Study Item Description (RP-170742), 3GPP, Dubrovnik, Croatia, Mar. 2017.

      5. Li, B., Fei, Z., Zhang, Y., UAV Communications for 5G and Beyond: Recent Advances and Future Trends. IEEE Internet Things J., 1, 1, 99, December 2018.

      6. Matolak, D.W. and Sun, R., Unmanned Aircraft Systems: Air-Ground Channel Characterization for Future Applications. IEEE Veh. Technol. Mag., 10, 2, 79–85, June 2015.

      7. Sun, R. and Matolak, D.W., Initial Results for Airframe Shadowing in L- and C-Band Air-Ground Channels. Proc. Integrated Commun., Navigation, and Surveillance Conf, Apr. 2015, pp. 1–8.

      8. 3GPP, Enhanced LTE support for aerial vehicles, May 2017.

      9. Khawaja, G. et al., A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles. IEEE Commun. Surv. Tutorials, 21, 3, 2361–2391, December 2019.

      10.

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