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Joint Resource Allocation and Trajectory Design for Resilient Multi-UAV Communication Networks

Linghui Ge, Xiao Liang, Hua Zhang, Peihao Dong, Jianxin Liao, Jingyu Wang

TL;DR

This letter proposes a resilient UAV network design utilizing the modern portfolio theory (MPT), which jointly optimizes the bandwidth allocation, UAV-user association, and UAV trajectories to enhance the overall service stability.

Abstract

In contrast to terrestrial wireless networks, dynamic Unmanned Aerial Vehicle (UAV) networks are susceptible to unexpected link failures arising from UAV breakdowns or the depletion of its batteries. Drastic user rate fluctuations and sum rate drops can occur due to the unexpected UAV link failures. Previous research has focused primarily on re-establishing these links to maintain service continuity, while neglecting overall system performance, including sum rate and user rate fluctuations. This letter proposes a resilient UAV network design utilizing the modern portfolio theory (MPT), which jointly optimizes the bandwidth allocation, UAV-user association, and UAV trajectories to enhance the overall service stability. Specifically, the design incorporates a novel utility function based on MPT to achieve a better balance between the sum rate and user rate fluctuations. To solve the joint optimization problem, we propose an iterative algorithm based on alternating optimization (AO) and successive convex approximation (SCA). Simulation results show that our scheme outperforms the other two baselines in terms of sum rate and user rate fluctuations. Furthermore, the resilience requirement in terms of sum rate, user rate fluctuations and user fairness can be achieved by flexibly tuning weight factor in our proposed algorithm.

Joint Resource Allocation and Trajectory Design for Resilient Multi-UAV Communication Networks

TL;DR

This letter proposes a resilient UAV network design utilizing the modern portfolio theory (MPT), which jointly optimizes the bandwidth allocation, UAV-user association, and UAV trajectories to enhance the overall service stability.

Abstract

In contrast to terrestrial wireless networks, dynamic Unmanned Aerial Vehicle (UAV) networks are susceptible to unexpected link failures arising from UAV breakdowns or the depletion of its batteries. Drastic user rate fluctuations and sum rate drops can occur due to the unexpected UAV link failures. Previous research has focused primarily on re-establishing these links to maintain service continuity, while neglecting overall system performance, including sum rate and user rate fluctuations. This letter proposes a resilient UAV network design utilizing the modern portfolio theory (MPT), which jointly optimizes the bandwidth allocation, UAV-user association, and UAV trajectories to enhance the overall service stability. Specifically, the design incorporates a novel utility function based on MPT to achieve a better balance between the sum rate and user rate fluctuations. To solve the joint optimization problem, we propose an iterative algorithm based on alternating optimization (AO) and successive convex approximation (SCA). Simulation results show that our scheme outperforms the other two baselines in terms of sum rate and user rate fluctuations. Furthermore, the resilience requirement in terms of sum rate, user rate fluctuations and user fairness can be achieved by flexibly tuning weight factor in our proposed algorithm.
Paper Structure (10 sections, 1 theorem, 29 equations, 4 figures, 1 algorithm)

This paper contains 10 sections, 1 theorem, 29 equations, 4 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. System Model
  4. Problem Formulation
  5. Resource Allocation and Trajectory Design
  6. UAV-user Association Problem
  7. Bandwidth Allocation Problem
  8. Design of UAV trajectories
  9. Simulation Results
  10. Conclusion

Key Result

Lemma 1

$\frac{1}{\mu }\log {E_n}\left( {\exp \left( {\mu {x_1}} \right) + \cdots + \exp \left( {\mu {x_n}} \right)} \right)$ is a concave function with ${\mu <0}$.

Figures (4)

  • Figure 1: Resilient Multi-UAV Network.
  • Figure 2: Convergence of the proposed algorithms for $K=9$.
  • Figure 3: Sum rate for different periods for $K=9$. (Solid line represents average sum rates every 10 time slots)
  • Figure 4: (a) Variance of rate versus $\mu$; (b) The Jain's fairness among users.

Theorems & Definitions (1)

  • Lemma 1