Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

An Improved Virtual Force Approach for UAV Deployment and Resource Allocation in Emergency Communications

Hongying Guo, Li Wang, Ruoguang Li, Luyang Hou, Lianming Xu, Aiguo Fei

TL;DR

The paper tackles UAV deployment and resource allocation for emergency communications in disaster environments with obstacles. It introduces an alternating optimization framework that fuses a coalition game for user association with a virtual force approach for deployment and power allocation, starting from K-Means initialization. The key contributions are a tractable, near-optimal solution to a non-convex NP-hard problem, explicit modeling of forest path loss and obstacle avoidance, and a demonstrated major reduction in computation time (about $5.6\%$ of heuristic baselines) while maintaining high transmission rates. The approach enables rapid, environment-aware UAV planning suitable for time-sensitive disaster response scenarios.

Abstract

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled emergency communication system, which establishes temporary communication link with users equipment (UEs) in a typical disaster environment with mountainous forest and obstacles. Towards this end, a joint deployment, power allocation, and user association optimization problem is formulated to maximize the total transmission rate, while considering the demand of each UE and the disaster environment characteristics. Then, an alternating optimization algorithm is proposed by integrating coalition game and virtual force approach which captures the impact of the demand priority of UEs and the obstacles to the flight path and consumed power. Simulation results demonstrate that the computation time consumed by our proposed algorithm is only $5.6\%$ of the traditional heuristic algorithms, which validates its effectiveness in disaster scenarios.

An Improved Virtual Force Approach for UAV Deployment and Resource Allocation in Emergency Communications

TL;DR

The paper tackles UAV deployment and resource allocation for emergency communications in disaster environments with obstacles. It introduces an alternating optimization framework that fuses a coalition game for user association with a virtual force approach for deployment and power allocation, starting from K-Means initialization. The key contributions are a tractable, near-optimal solution to a non-convex NP-hard problem, explicit modeling of forest path loss and obstacle avoidance, and a demonstrated major reduction in computation time (about of heuristic baselines) while maintaining high transmission rates. The approach enables rapid, environment-aware UAV planning suitable for time-sensitive disaster response scenarios.

Abstract

In this paper, we consider an unmanned aerial vehicle (UAV)-enabled emergency communication system, which establishes temporary communication link with users equipment (UEs) in a typical disaster environment with mountainous forest and obstacles. Towards this end, a joint deployment, power allocation, and user association optimization problem is formulated to maximize the total transmission rate, while considering the demand of each UE and the disaster environment characteristics. Then, an alternating optimization algorithm is proposed by integrating coalition game and virtual force approach which captures the impact of the demand priority of UEs and the obstacles to the flight path and consumed power. Simulation results demonstrate that the computation time consumed by our proposed algorithm is only of the traditional heuristic algorithms, which validates its effectiveness in disaster scenarios.
Paper Structure (13 sections, 15 equations, 5 figures, 2 algorithms)

This paper contains 13 sections, 15 equations, 5 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Proposed Solution
  6. K-Means for UAV Deployment Initialization
  7. Coalition Game for User Association
  8. Virtual Force Approach for UAV Deployment and Power Allocation
  9. The Attractive Force Towards UEs
  10. The Repulsive Force Between UAVs
  11. The Repulsive Force to Avoid Obstacle
  12. Simulation Results
  13. Conclusion

Figures (5)

  • Figure 1: Illustration of virtual forces.
  • Figure 2: The results of UAV deployment, power allocation, and user association.
  • Figure 3: The convergence behavior of the proposed algorithm and its benchmarks.
  • Figure 4: Total transmission rate versus the number of UEs.
  • Figure 5: Computation time versus the number of UEs.

Theorems & Definitions (2)

  • Definition 1
  • Definition 2