Trends and Challenges of Autonomous Drones in Enabling Resilient Telecommunication Networks

S. Ismail, Laith; Naseer Faraj, Lydia; Mamytovich Zholdoshov, Tolkunbek; Hashim Qasim, Nameer; Abdullah Hafedh, Milad

doi:10.22034/jipm.2025.728438

Trends and Challenges of Autonomous Drones in Enabling Resilient Telecommunication Networks

Document Type : Original Article

Authors

Laith S. Ismail ¹

Lydia Naseer Faraj ²

Tolkunbek Mamytovich Zholdoshov ³

Nameer Hashim Qasim ⁴

Milad Abdullah Hafedh ⁵

¹ Al-Turath University, Baghdad 10013, Iraq

² Al-Mansour University College, Baghdad 10067, Iraq

³ Osh State University, Osh City 723500, Kyrgyzstan

⁴ Al-Rafidain University College Baghdad, Iraq,10064

⁵ Madenat Alelem University College, Baghdad 10006, Iraq

10.22034/jipm.2025.728438

Abstract

ABSTRACT
Background: The advances in use of resilient telecommunication networks have shown the possible use of autonomous drones to support connectivity in unpredictable and complex terrains. Current network infrastructures have limitations in delivering optimized service in areas like traffic congestion, area of sparseness, disasters etc., which requires some form of innovation.
Objective: The article is meant to propose a framework for using autonomous drones in practical telecommunication systems, with emphasis on the energy consumption, scalability, dependability, and flexibility of the solution for various situations.
Methods: The study also uses other state-of-the-art approaches such as trajectory optimization, swarm coordination, dynamic spectrum management, and machine learning based resource allocation. Various slips were used on urban, rural, and disaster-sensitive scenarios to assess performance indices including energy input, network connectivity, signal strength, and lag time. The simulation results were supported by field experiments providing insights into various circumstances.
Results: The simulation results of the actually proposed framework show network scalability enhancements, where coverage area involves up to 50 km² and power saving higher than 15%. The performance improvement included near perfect trajectory anticipation at a rate of 98%, while the utilization of resources was also optimized. Dynamic spectrum management was useful in reducing interference and increasing efficiency especially in areas of high density.
Conclusion: The article promotes the use of UAV based telecommunication networks where challenging questions on scalability and reliability are raised and solved. Through the work presented, strong theoretical and empirical assumptions are made to foster concepts that will solidify next generation communication network.

Keywords

KEYWORDS: autonomous drones

UAVs

telecommunication networks

trajectory optimization

swarm coordination

dynamic spectrum management (DSM)

machine learning

energy efficiency

network scalability

disaster recovery

References

Alsamhi, S. H., Almalki, F. A., Ma, O., Ansari, M. S., and Lee, B. (2023). Predictive Estimation of Optimal Signal Strength From Drones Over IoT Frameworks in Smart Cities. IEEE Transactions on Mobile Computing, 22 (1), 402-416. https://doi.org/:10.1109/TMC.2021.3074442

Amponis, G., Lagkas, T., Zevgara, M., Katsikas, G., Xirofotos, T., Moscholios, I., and Sarigiannidis, P. (2022). Drones in B5G/6G Networks as Flying Base Stations. Drones, 6 (2). https://doi.org/:10.3390/drones6020039.

Arani, A. H., Azari, M. M., Hu, P., Zhu, Y., Yanikomeroglu, H., and Safavi-Naeini, S. (2022). Reinforcement Learning for Energy-Efficient Trajectory Design of UAVs. IEEE Internet of Things Journal, 9 (11), 9060-9070. https://doi.org/:10.1109/JIOT.2021.3118322

Chen, X., Wang, H., Li, Z., Ding, W., Dang, F., Wu, C., and Chen, X. (2023). DeliverSense: Efficient Delivery Drone Scheduling for Crowdsensing with Deep Reinforcement Learning. Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers, Cambridge, United Kingdom. https://doi.org/:10.1145/3544793.3560412

Fakhreddine, A., Raffelsberger, C., Sende, M., and Bettstetter, C. (2022). Experiments on Drone-to-Drone Communication with Wi-Fi, LTE-A, and 5G. 2022 IEEE Globecom Workshops (GC Wkshps), 4-8 Dec. 2022. https://doi.org/:10.1109/GCWkshps56602.2022.10008743.

Gohari, A., Ahmad, A. B., Rahim, R. B. A., Supa’at, A. S. M., Razak, S. A., and Gismalla, M. S. M. (2022). Involvement of Surveillance Drones in Smart Cities: A Systematic Review. IEEE Access, 10, 56611-56628. https://doi.org/:10.1109/ACCESS.2022.3177904

Gopi, S. P., Magarini, M., Alsamhi, S. H., and Shvetsov, A. V. (2021). Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G. Drones, 5 (4). https://doi.org/:10.3390/drones5040128.

Han, T., Ribeiro, I. d. L., Magaia, N., Preto, J., Segundo, A. H. F. N., Macêdo, A. R. L. d., Muhammad, K., et al. (2021). Emerging Drone Trends for Blockchain-Based 5G Networks: Open Issues and Future Perspectives. IEEE Network, 35 (1), 38-43. https://doi.org/:10.1109/MNET.011.2000151

Hashesh, A. O., Hashima, S., Zaki, R. M., Fouda, M. M., Hatano, K., and Eldien, A. S. T. (2022). AI-Enabled UAV Communications: Challenges and Future Directions. IEEE Access, 10, 92048-92066. https://doi.org/:10.1109/ACCESS.2022.3202956

Huang, H., and Savkin, A. V. (2022). Deployment of Heterogeneous UAV Base Stations for Optimal Quality of Coverage. IEEE Internet of Things Journal, 9 (17), 16429-16437. https://doi.org/:10.1109/JIOT.2022.3150292

Jacobsen, R. H., Matlekovic, L., Shi, L., Malle, N., Ayoub, N., Hageman, K., Hansen, S., et al. (2023). Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure. Applied Sciences, 13 (3). https://doi.org/:10.3390/app13031256.

Jawad Aqeel Mahmood, Q. N. H., Jawad Haider Mahmood, Abu-Alshaeer Mahmood Jawad, Nordinc Rosdiadee, Gharghand Sadik Kamel (2022). Near Field WPT Charging a Smart Device Based on IoT Applications. CEUR. https://ceur-ws.org/Vol-3149/paper7.pdf

Khan, M. A., Kumar, N., Mohsan, S. A. H., Khan, W. U., Nasralla, M. M., Alsharif, M. H., Żywiołek, J., et al. (2023). Swarm of UAVs for Network Management in 6G: A Technical Review. IEEE Transactions on Network and Service Management, 20 (1), 741-761. https://doi.org/:10.1109/TNSM.2022.3213370

Li, K., Ni, W., and Dressler, F. (2022). Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks. IEEE Transactions on Mobile Computing, 21 (8), 2732-2744. https://doi.org/:10.1109/TMC.2021.3049178

Li, X., Cheng, S., Ding, H., Pan, M., and Zhao, N. (2023). When UAVs Meet Cognitive Radio: Offloading Traffic Under Uncertain Spectrum Environment via Deep Reinforcement Learning. IEEE Transactions on Wireless Communications, 22 (2), 824-838. https://doi.org/:10.1109/TWC.2022.3198665

Li, Y., and Liu, M. (2022). Path Planning of Electric VTOL UAV Considering Minimum Energy Consumption in Urban Areas. Sustainability, 14 (20). https://doi.org/:10.3390/su142013421.

Musovic, J., Lipovac, A., and Lipovac, V. (2022). BER Aided Energy and Spectral Efficiency Estimation in a Heterogeneous Network. Computation, 10 (9). https://doi.org/:10.3390/computation10090162.

Pal, P., Sharma, R. P., Tripathi, S., Kumar, C., and Ramesh, D. (2022). Machine Learning Regression for RF Path Loss Estimation Over Grass Vegetation in IoWSN Monitoring Infrastructure. IEEE Transactions on Industrial Informatics, 18 (10), 6981-6990. https://doi.org/:10.1109/TII.2022.3142318

Qasim, N. (2019). New Approach to the Construction of Multimedia Test Signals. International Journal of Advanced Trends in Computer Science and Engineering, 8, 3423-3429. https://doi.org/:10.30534/ijatcse/2019/117862019

Qasim, N., and Pyliavskyi, V. (2020). Color temperature line: forward and inverse transformation. Semiconductor physics, quantum electronics and optoelectronics, 23, 75-80. https://doi.org/:10.15407/spqeo23.01.075

Qasim, N. H., Al-Helli, H.I., Savelieva, I., Jawad, A. M. (2023). Modern Ships and the Integration of Drones – a New Era for Marine Communication. Development of Transport, 4 (19). https://doi.org/:10.33082/td.2023.4-19.05

Qasim, N. H., and Jawad, A. M. (2024). 5G-enabled UAVs for energy-efficient opportunistic networking. Heliyon, 10 (12), e32660. https://doi.org/:10.1016/j.heliyon.2024.e32660

Savkin, A. V., and Huang, H. (2021). Range-Based Reactive Deployment of Autonomous Drones for Optimal Coverage in Disaster Areas. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51 (7), 4606-4610. https://doi.org/:10.1109/TSMC.2019.2944010

Shayea, I., Dushi, P., Banafaa, M., Rashid, R. A., Ali, S., Sarijari, M. A., Daradkeh, Y. I., et al. (2022). Handover Management for Drones in Future Mobile Networks—A Survey. Sensors, 22 (17). https://doi.org/:10.3390/s22176424.

Si-Mohammed, S., Bouaziz, M., Hellaoui, H., Bekkouche, O., Ksentini, A., Taleb, T., Tomaszewski, L., et al. (2021). Supporting Unmanned Aerial Vehicle Services in 5G Networks: New High-Level Architecture Integrating 5G With U-Space. IEEE Vehicular Technology Magazine, 16 (1), 57-65. https://doi.org/:10.1109/MVT.2020.3036374

Tarekegn, G. B., Juang, R. T., Lin, H. P., Munaye, Y. Y., Wang, L. C., and Bitew, M. A. (2022). Deep-Reinforcement-Learning-Based Drone Base Station Deployment for Wireless Communication Services. IEEE Internet of Things Journal, 9 (21), 21899-21915. https://doi.org/:10.1109/JIOT.2022.3182633

Wu, Y., Low, K. H., Pang, B., and Tan, Q. (2021). Swarm-Based 4D Path Planning For Drone Operations in Urban Environments. IEEE Transactions on Vehicular Technology, 70 (8), 7464-7479. https://doi.org/:10.1109/TVT.2021.3093318