An optimized LTE-based technique for drone base station 3D placement and resource allocation in delay-sensitive M2M networks
I would like to express my sincere thanks and gratitude to my thesis advisor, Dr. Yasser Gadallah, for his tremendous support in completing this research work. I remember every piece of advice in our meetings and discussions that always built a strong and solid foundation for the thesis in particular and for my research skills in general. It is also worth mentioning the endless support of my academic advisor and the masterâ€™s program director, Dr. Maki Habib, for his exerted efforts to facilitate and grant the financial assistance that was a broad base of support to complete my study in the RCSS program. In addition, I am so grateful for the time spent in his labs where I learned many practical and professional concepts and methods in teaching and conducting research through my work as a teaching and research assistant under his guidance and supervision. My special and sincere thank you also goes to my dear colleague, Heba Kadry, for her continued moral support and encouragement throughout the years of working on my thesis. I remember when we spent hours discussing the challenges of the thesis or even the complicated coursework and how these discussions ended with a significant push to stand and move forward towards achieving my academic potential. Finally and foremost, many thanks to the great deal of support and assistance that grant me the completion of this thesis work and my masterâ€™s degree.
The deployment of drone-mounted communication systems has received increasing interest and attention recently as it allows significant improvement to the network access capacity and coverage. Many applications can benefit from such deployments in particular machine-to-machine (M2M) communications. Drones are expected to facilitate extending wireless network access for both human users and the smart machine-type-communication devices (MTCDs) that have strict and diverse quality of service (QoS) requirements. In this thesis, we propose an optimal solution for the dynamic placement of an LTE drone-mounted base station to maximize the coverage of MTCDs deployed over a large geographical area in disaster situations or within remote applications. This solution considers the strict data transmission deadlines of some of the deployed MTCDs. The resulting technique thus jointly optimizes the drone’s 3D positioning to maximize coverage and allocates the network resources in such a way that gives high priority to the delay-sensitive M2M traffic. This optimization algorithm determines the optimal bound of the solution. Since it cannot be used in real-time operations due to its computational complexity, we also introduce a heuristic technique that offers a near-optimal solution with reduced complexity to be used in real-time arrangements. The proposed exact optimization algorithm utilizes the outer-approximation technique along with the penalty method in search of the global optimal point in the problem feasible space. On the other hand, the heuristic solver employs the swarm intelligence technique to reach to the near-optimal potential solution. We conduct several simulation experiments to evaluate the proposed techniques. The network communication performance is measured with regards to the system throughput gains and the occurrences of missing transmission deadlines. In addition, the overall network coverage is represented in terms of the average signal-to-noise ratio (SNR) of the deployed MTCDs. These results are compared to those of other drone placement approaches. The comparisons show that the proposed techniques offer significantly better results in the communication coverage while fulfilling the diverse QoS requirements of the deployed M2M network. Moreover, the heuristic technique is shown to succeed in finding a solution that is close to the optimal bound with a considerable reduction of complexity over the exact optimization algorithm.