Placement Optimization of UAV-Mounted Mobile Base Stations (1612.01047v1)

Abstract: In terrestrial communication networks without fixed infrastructure, unmanned aerial vehicle (UAV)-mounted mobile base stations (MBSs) provide an efficient solution to achieve wireless connectivity. This letter aims to minimize the number of MBSs needed to provide wireless coverage for a group of distributed ground terminals (GTs), ensuring that each GT is within the communication range of at least one MBS. We propose a polynomial-time algorithm with successive MBS placement, where the MBSs are placed sequentially starting on the area perimeter of the uncovered GTs along a spiral path towards the center, until all GTs are covered. Each MBS is placed to cover as many uncovered GTs as possible, with higher priority given to the GTs on the boundary to reduce the occurrence of outlier GTs that each may require one dedicated MBS for its coverage. Numerical results show that the proposed algorithm performs favorably compared to other schemes in terms of the total number of required MBSs and/or time complexity.

• The paper introduces a spiral-based algorithm that minimizes the number of UAV-mounted base stations while ensuring full wireless coverage of ground terminals.
• It employs a sequential placement strategy prioritizing boundary ground terminals by moving inward in a spiral path to reduce redundant coverage.
• Numerical simulations show that for 80 GTs over a 10 km² area, the algorithm achieved near-optimal performance compared to methods like K-means clustering.

Placement Optimization of UAV-Mounted Mobile Base Stations

Introduction

The paper "Placement Optimization of UAV-Mounted Mobile Base Stations" addresses the significant challenge of providing wireless coverage in areas lacking fixed communication infrastructure using unmanned aerial vehicle (UAV)-mounted mobile base stations (MBSs). This necessity often arises in scenarios such as battlefields or disaster-stricken areas. The objective is to minimize the number of MBSs required to ensure that all ground terminals (GTs) are within the communication range of at least one MBS, a problem formulated as the Geometric Disk Cover (GDC) problem.

Methodology

The authors propose a polynomial-time algorithm for successive MBS placement. The approach begins with placing MBSs sequentially on the periphery of the uncovered GT area and proceeds inwardly along a spiral path towards the center until coverage is attained for all GTs. Priority is given to boundary GTs to minimize occurrences of outlier GTs, which could potentially necessitate additional dedicated MBSs, thus optimizing coverage efficiency.

The key steps of the proposed algorithm include:

1. Identifying boundary GTs and ordering them counterclockwise.
2. Placing an MBS to cover the boundary GTs and adjusting its position inward to maximize coverage.
3. Repeating the above for remaining GTs until full coverage is achieved.

This spiral placement strategy ensures low computational complexity, making it feasible for larger networks, with a worst-case complexity of $O(K^3)$, where $K$ is the number of GTs.

Numerical Results and Performance

The authors present comprehensive numerical simulations to validate their algorithm. Key metrics are the number of MBSs required and the computational time. For small networks where the optimal solution can be determined through core-sets method, the proposed algorithm demonstrated near-optimal performance with significantly lower computational requirements.

In the specific scenario of 80 GTs distributed over a 10 km² area, the spiral algorithm required 11 MBSs, which aligns with the theoretical minimum found using the core-sets method. Moreover, it outperforms other heuristic schemes such as K-means clustering and strip-based algorithms in terms of both the number of required MBSs and computational efficiency.

Practical and Theoretical Implications

The spiral MBS placement algorithm provides a scalable and efficient solution for establishing wireless networks in infrastructure-lacking regions. The primary practical implication is its potential application in emergency scenarios, ensuring reliable communication can be quickly established. Theoretically, this work contributes to the field by presenting a novel heuristic that offers a significant trade-off between solution accuracy and computational feasibility for the NP-hard GDC problem.

Future Developments

Future research directions would likely explore extensions of the proposed algorithm to incorporate backhaul connectivity constraints and adapt to dynamic environments with moving GTs. Integrating real-time data into the placement decisions and considering multi-access interference or power control aspects could further enhance the applicability of the proposed method in realistic deployment scenarios.

Conclusion

The paper delivers an efficient MBS placement algorithm with polynomial-time complexity, effectively minimizing the number of required MBSs to ensure comprehensive coverage of GTs in areas devoid of fixed infrastructure. This paper underscores the utility and efficiency of using UAV-mounted MBSs for rapid deployment of wireless communication systems in exigent situations, contributing a valuable tool to the field of UAV-GT communications.