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An Optimized LTE-Based Technique for Drone Base Station 3D Placement and Resource Allocation in Delay-Sensitive M2M Networks
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. D...
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| Format: | Thesis |
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AUC Knowledge Fountain
2021
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| Summary: | 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. |
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