Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage (1606.01962v1)

Abstract: In this paper, the efficient deployment of multiple unmanned aerial vehicles (UAVs) with directional antennas acting as wireless base stations that provide coverage for ground users is analyzed. First, the downlink coverage probability for UAVs as a function of the altitude and the antenna gain is derived. Next, using circle packing theory, the three-dimensional locations of the UAVs is determined in a way that the total coverage area is maximized while maximizing the coverage lifetime of the UAVs. Our results show that, in order to mitigate interference, the altitude of the UAVs must be properly adjusted based on the beamwidth of the directional antenna as well as coverage requirements. Furthermore, the minimum number of UAVs needed to guarantee a target coverage probability for a given geographical area is determined. Numerical results evaluate the various tradeoffs involved in various UAV deployment scenarios.

• The paper introduces a circle packing-based method to determine optimal 3D UAV positions for maximizing downlink coverage and efficiency.
• The study derives downlink coverage probability models that incorporate UAV altitude, antenna gain, and probabilistic LoS/NLoS links for precise deployment.
• Simulation results reveal a trade-off between UAV count and coverage lifetime, with optimal altitudes decreasing as more UAVs are deployed.

Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage

The paper "Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage" addresses a critical issue in wireless communication networks: the optimal deployment of multiple unmanned aerial vehicles (UAVs) equipped with directional antennas to serve as aerial base stations. This work significantly contributes to the UAV-based wireless communication field by providing a detailed analysis of various trade-offs between UAV deployment parameters and their impact on coverage performance.

Summary

The paper starts by establishing the importance of UAVs as aerial base stations due to their advantages over terrestrial base stations, such as higher chances of line-of-sight (LoS) links and flexible deployment capabilities. Despite these benefits, the authors identify several technical challenges, notably the optimal three-dimensional (3D) deployment of UAVs to balance energy consumption, and interference management.

The primary focus of the paper is the derivation of downlink coverage probability for UAVs based on their altitude and antenna gain. Leveraging circle packing theory, the paper proposes an efficient deployment strategy for determining the 3D positions of UAVs to maximize total coverage while also optimizing the UAVs' coverage lifetime.

Coverage Probability and Optimal Deployment

The authors present a thorough derivation of the downlink coverage probability, incorporating factors such as the UAV's altitude, antenna gain, path loss, and probabilistic LoS/NLoS links. The formulation considers the specific probabilistic model: $$P_{\text{LoS},j} = \alpha {\left( \frac{180}{\pi} \theta_j - 15 \right)}^\gamma,$$ where $\alpha$ and $\gamma$ reflect environmental impacts, and $\theta_j$ is the elevation angle.

The optimal deployment method involves using circle packing theory to avoid overlapping coverage areas, thereby minimizing interference. By solving the circle packing problem for various numbers of UAVs, the authors can determine the necessary coverage radius for each UAV and their optimal heights: $h = \frac{r_u}{\tan (\frac{\theta_B}{2})}$ This yields the optimal 3D deployment of UAVs for a given geographical area and coverage requirement.

Numerical Results

The simulation results provide deep insights into the practical aspects of UAV deployment:

1. Coverage and Lifetime Trade-off: For a geographical area with a radius of 5000 meters, the paper finds an interplay between the number of UAVs and their coverage lifetime. Single UAVs can cover the maximum area but have minimal coverage lifetime, while more UAVs increase coverage lifetime but decrease per-UAV coverage.
2. Optimal Altitude: As the number of UAVs increases, the optimal altitude must decrease to ensure non-overlapping coverage. For example, increasing the number of UAVs from 3 to 6 reduces the optimal altitude from 2000 meters to 1300 meters.
3. Minimum Number of UAVs: The paper determines the minimum number of UAVs required to meet a target coverage probability for various sizes of the area. Notably, to achieve a 0.7 coverage threshold for specific area sizes, one UAV or more than six UAVs are necessary.

Implications and Future Work

Practically, this research serves as a framework for deploying UAVs as aerial base stations, ensuring efficient communication infrastructure, especially in scenarios such as temporary hotspots or disaster recovery. The paper’s approach to determining the optimal deployment considering coverage probability and interference mitigation is highly relevant for ongoing and future 5G and beyond networks where UAVs are expected to play a crucial role.

Theoretically, the results underscore the importance of probabilistic modeling in optimizing UAV placement, which could inspire further research. Future work may include heterogeneous UAV deployments with varying altitudes, transmit powers, and antenna gains, or investigating different geometrical configurations for the target area.

In conclusion, this paper presents a rigorous and systematic approach for the efficient deployment of multiple UAVs to maximize wireless coverage. By balancing various deployment parameters and leveraging mathematical theories such as circle packing, the authors provide a highly relevant contribution to the UAV-based wireless communication literature.