Spectrum Sharing between UAV-based Wireless Mesh Networks and Ground Networks

Abstract: The unmanned aerial vehicle (UAV)-based wireless mesh networks can economically provide wireless services for the areas with disasters. However, the capacity of air-to-air communications is limited due to the multi-hop transmissions. In this paper, the spectrum sharing between UAV-based wireless mesh networks and ground networks is studied to improve the capacity of the UAV networks. Considering the distribution of UAVs as a three-dimensional (3D) homogeneous Poisson point process (PPP) within a vertical range, the stochastic geometry is applied to analyze the impact of the height of UAVs, the transmit power of UAVs, the density of UAVs and the vertical range, etc., on the coverage probability of ground network user and UAV network user, respectively. The optimal height of UAVs is numerically achieved in maximizing the capacity of UAV networks with the constraint of the coverage probability of ground network user. This paper provides a basic guideline for the deployment of UAV-based wireless mesh networks.

• The paper presents a stochastic geometry framework to analyze and improve spectrum sharing between UAV and ground networks.
• It models UAV distributions using a 3D Poisson point process to derive optimal heights that balance network capacity and ground user coverage.
• Numerical simulations show that minimizing vertical range and adjusting UAV altitude maximizes transmission capacity while meeting coverage constraints.

Spectrum Sharing between UAV-based Wireless Mesh Networks and Ground Networks

Introduction

The paper "Spectrum Sharing between UAV-based Wireless Mesh Networks and Ground Networks" offers a rigorous analysis of spectrum sharing protocols between Unmanned Aerial Vehicle (UAV)-based wireless mesh networks and ground networks to enhance the capacity of the UAV networks. The authors leverage stochastic geometry to assess various parameters affecting coverage probabilities for UAV and ground network users. This study provides a valuable guideline for the optimal deployment of UAV-based wireless mesh networks.

Problem Statement and Motivation

UAV-mounted base stations have proved instrumental in providing wireless services, especially in disaster-stricken areas where ground infrastructure is compromised. Despite this utility, the capacity of air-to-air communication remains restricted due to multi-hop transmissions. The paper addresses the critical issue of improving the capacity of UAV networks by exploring spectrum sharing between UAV-based wireless mesh networks and ground networks. Unlike previous efforts primarily focusing on spectrum sharing between UAVs and other wireless systems, this study explores spectrum sharing between UAV-based mesh networks and ground networks.

Methodology

The authors model the distribution of UAVs as a three-dimensional (3D) homogeneous Poisson point process (PPP) within a vertical range. Stochastic geometry analysis is employed to evaluate the impact of multiple parameters such as the UAV height, transmit power, density, and vertical range on the coverage probabilities for both UAV and ground network users. Several key formulations are provided to derive the coverage probabilities and optimal UAV heights numerically, balancing the dual objectives of maximizing UAV network capacity while ensuring adequate ground network user coverage.

Key Findings

1. Coverage Probability Analysis:
   • The coverage probability of a typical ground network user (P1) and UAV network user (P2) are derived using stochastic geometry. Higher UAV altitude generally decreases interference for ground users, thus increasing P1, whereas specific optimal heights maximize P2 due to fluctuating signal and interference characteristics.
2. Impact of Vertical Range:
   • A prominent finding is that when the UAV vertical range (Δh) approaches zero, UAVs behave as if distributed in a two-dimensional plane, thereby maximizing the network's transmission capacity (TC).
3. Optimal Height Numerical Achievement:
   • Through numerical simulations, an optimal height for UAV deployment is determined, which maximizes the UAV network's transmission capacity while constraining the ground network users' coverage probability. For instance, with a ground network coverage probability constraint (α = 0.4), the study numerically derives the optimal height that yields the best performance for UAV networks.

Numerical Results

The simulations corroborate the theoretical findings and provide practical insights into UAV deployment. Several key trends are observed:

1. Ground Network User Coverage:
   • When h1 (minimum UAV height) is large, ground user interference from UAVs decreases, increasing P1. However, greater vertical ranges reduce P1 due to increased likelihood of line-of-sight (LoS) interference.
2. UAV Network User Coverage:
   • The coverage probability P2 shows non-linear behavior with varying h1. Optimal h1 exists that maximizes P2 for any given Δh.
3. Transmission Capacity Maximization:
   • Transmission capacity improves as Δh decreases, indicating that minimal vertical distribution benefit the UAV network performance. The optimal h1 varies based on the set coverage probability constraints for ground users.

Implications and Future Directions

The findings have significant practical and theoretical implications. Practically, the paper provides a foundational guideline for deploying UAV-based wireless mesh networks, particularly in disaster management and high-traffic scenarios like concerts. The outcome can enhance network designers' ability to optimize UAV deployment for maximal coverage and capacity. Theoretically, the study enriches the body of knowledge in stochastic geometry applications in wireless networks, positioning itself as a cornerstone for future research in spectrum sharing technologies.

Future research directions may include exploring dynamic spectrum sharing strategies, incorporating machine learning for real-time optimization, and extending the models to heterogeneous UAV networks with different altitude layers and mobility patterns. These advancements could foster more resilient and efficient UAV-based communication systems.

Conclusion

The research contributes robust stochastic geometry-based analytical frameworks for improving UAV network capacities through strategic spectrum sharing with ground networks. Additionally, it delineates the deployment parameters critical for maximizing both UAV and ground network user coverage probabilities. This study lays a strong foundation for the strategic deployment and optimization of UAV-based wireless mesh networks, crucial for enhancing wireless communication resilience and capacity.