Wireless Coverage for Mobile Users in Dynamic Environments Using UAV

Ahmad Sawalmeh

doi:10.1109/ACCESS.2019.2938272

2019-01-01

Ahmad Sawalmeh

Noor Shamsiah Othman *

Hazim Shakhatreh

Abdallah Khreishah

DOI：10.1109/ACCESS.2019.2938272

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摘要

En 中文

In this paper, the dynamic deployment of a single UAV as an aerial base station in providing wireless coverage for mobile outdoor and indoor users is studied. The problem of finding the efficient UAV trajectory is formulated with the objective to minimize the required UAV transmit power that satisfies the users' minimum data rate. The proposed solution to the problem considers the users' movement in a search and rescue (SAR) operation. More specifically, the outdoor rescue team members are considered to move in a group with the reference point group mobility (RPGM) model. Whilst, the indoor rescue team members are considered to move individually and in a group with random waypoint and RPGM models, respectively. The efficient UAV trajectory is developed using two approaches, namely, heuristic and optimal approaches. The employment of the heuristic approach, namely particle swarm optimization (PSO) and genetics algorithm (GA), to find the efficient UAV trajectory reduced the execution time by a factor of similar or equal to 1/60 and similar or equal to 1/9 compared to that when using the optimal approach of brute-force search space algorithm. Furthermore, the use of PSO algorithm reduced the execution time by a factor of similar or equal to 1/7 compared to that when the GA algorithm is invoked. The performance of the dynamic UAV deployment also outperformed the static UAV deployment in terms of the required transmit power. More specifically, the dynamic UAV deployment required less total transmit power by a factor of about 1/2 compared to the static UAV deployment, in providing wireless coverage for rescue team to perform SAR operation within a rectangular sub-region.

Keyword:

Genetic algorithm

particle swarm optimization

random waypoint

reference point group mobility model

unmanned aerial vehicles