Efficient 3-D Placement of an Aerial Base Station in Next Generation Cellular Networks (1603.00300v1)

Abstract: Agility and resilience requirements of future cellular networks may not be fully satisfied by terrestrial base stations in cases of unexpected or temporary events. A promising solution is assisting the cellular network via low-altitude unmanned aerial vehicles equipped with base stations, i.e., drone-cells. Although drone-cells provide a quick deployment opportunity as aerial base stations, efficient placement becomes one of the key issues. In addition to mobility of the drone-cells in the vertical dimension as well as the horizontal dimension, the differences between the air-to-ground and terrestrial channels cause the placement of the drone-cells to diverge from placement of terrestrial base stations. In this paper, we first highlight the properties of the dronecell placement problem, and formulate it as a 3-D placement problem with the objective of maximizing the revenue of the network. After some mathematical manipulations, we formulate an equivalent quadratically-constrained mixed integer non-linear optimization problem and propose a computationally efficient numerical solution for this problem. We verify our analytical derivations with numerical simulations and enrich them with discussions which could serve as guidelines for researchers, mobile network operators, and policy makers.

• The paper introduces a 3-D optimization framework using mixed integer non-linear programming to maximize drone-cell coverage and revenue.
• It employs an interior-point optimizer paired with a bisection search to determine efficient placement in complex urban air-to-ground channels.
• Numerical results demonstrate enhanced network performance, especially in suburban areas compared to high-rise urban environments.

Efficient 3-D Placement of an Aerial Base Station in Next Generation Cellular Networks

The paper "Efficient 3-D Placement of an Aerial Base Station in Next Generation Cellular Networks" by Yaliniz, El-Keyi, and Yanikomeroglu addresses a critical challenge in the deployment of low-altitude drone-cells for future cellular networks. The authors investigate the optimal 3-D placement of drone-cells to maximize network coverage and revenue, particularly under scenarios requiring rapid and temporary deployments, such as natural disasters or events with extreme user densities.

Problem Formulation and Approach

The authors formulate the drone-cell placement as a 3-D optimization problem, considering both the horizontal (2-D) and vertical (altitude) dimensions. The primary objective is to maximize network revenue by serving the maximum number of users from the drone-cell. This necessitates modeling the air-to-ground channel properties, which differ significantly from those of terrestrial channels due to higher probabilities of line-of-sight (LoS) connectivity and urban environmental factors.

Their optimization problem is then transformed into a quadratically-constrained mixed integer non-linear programming (MINLP) problem. This transformation involves introducing a new variable that captures the relationship between the drone-cell altitude and its coverage radius. Due to the complex nature of the formulated channel model, the authors employ numerical methods for deriving an efficient and practical solution. Notably, they propose an interior-point optimizer paired with a one-dimensional bisection search to streamline obtaining the optimal placement.

Analytical and Numerical Results

The paper presents detailed numerical simulations validating the theoretical derivations. Different urban environments (e.g., suburban, urban, dense urban, and high-rise urban) were analyzed to understand the impact of environmental parameters on drone-cell coverage. The results indicate that suburban environments achieve larger coverage areas due to lower blockage effects compared to high-rise urban settings.

For instance, the mean number of users covered varied significantly based on the Quality of Service (QoS) requirements, classified under pathloss thresholds ($\gamma_1, \gamma_2, \gamma_3$). With $\gamma_3$ set at 125 dB, the drone-cell could cover more users, sometimes exceeding the macrocell's own capacity, illustrating the flexibility and utility of drone-cells in scenarios demanding rapid network augmentation.

Practical and Theoretical Implications

This paper contributes several practical implications for mobile network operators (MNOs) and policy makers:

1. Network Resilience and Agility: Drone-cells offer an agile response mechanism for network disruptions, ensuring QoS standards are maintained during infrastructural failures or peak loads.
2. Optimal Deployment Strategies: The efficient placement algorithms and models delineated in this paper provide a blueprint for deploying drone-cells in varying urban environments, considering unique environmental constraints and user distributions.
3. Guidance for Future Regulations: Insights from this work can inform public policies and regulations regarding the use of UAVs in commercial telecommunication services, ensuring optimized and safe deployment strategies.

Future Research Directions

Future research may explore:

1. Interference Management: Investigating the effects of introducing multiple drone-cells and developing interference mitigation strategies can expand the efficacy of drone-assisted networks.
2. Dynamic User Distributions: Extending the models to account for dynamic and non-uniform user distributions can yield more adaptable and resilient network performance metrics.
3. Energy Consumption and Sustainability: Evaluating the energy consumption profiles of drone-cells and developing more efficient energy management strategies to ensure sustainable operation.

In conclusion, the paper presents a comprehensive and practical approach to optimizing the placement of drone-cells for next-generation cellular networks. By illustrating both the theoretical foundations and practical implications, it sets a strong foundation for future advancements in this domain.