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UAV Swarm-enabled Collaborative Post-disaster Communications in Low Altitude Economy via a Two-stage Optimization Approach

Xiaoya Zheng, Geng Sun, Jiahui Li, Jiacheng Wang, Qingqing Wu, Dusit Niyato, Abbas Jamalipour

TL;DR

This work tackles post-disaster communications in the low-altitude economy by proposing a UAV-swarm network that uses collaborative beamforming to relay data from a ground device to a remote AP. It introduces a two-stage optimization: Stage One derives an optimal traffic routing and a theoretical upper bound on the network rate via a maximum-flow formulation, solvable with Ford-Fulkerson, yielding an optimized routing graph and upper bound $\overline{R}_{0,N_{ m S}+1}$. Stage Two transforms the problem into a penalty-based variant (V-RPTRMOP) and solves it with diffusion-model-enabled PSO (DM-PSO) to tune UAV placements and excitation weights so the actual rate approaches the upper bound. Simulations demonstrate significant rate improvements, routing superiority over classical protocols, and robustness to UAV position jitters and phase errors, highlighting practical potential for rapid disaster response. The approach provides a scalable, robust framework for cooperative UAV networks in emergency settings, balancing throughput, energy, and reliability.

Abstract

The low-altitude economy (LAE) plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communication. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. In this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP). Then, we propose a two-stage optimization approach to address it. In the first stage, the optimal traffic routing and the theoretical upper bound on the transmission rate of the network are derived. In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP, and a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm is proposed to deal with the V-RPTRMOP. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the constructed network, which demonstrates the great potential for post-disaster communications. Moreover, the robustness of the constructed network is also validated via evaluating the impact of two unexpected situations on the system transmission rate.

UAV Swarm-enabled Collaborative Post-disaster Communications in Low Altitude Economy via a Two-stage Optimization Approach

TL;DR

This work tackles post-disaster communications in the low-altitude economy by proposing a UAV-swarm network that uses collaborative beamforming to relay data from a ground device to a remote AP. It introduces a two-stage optimization: Stage One derives an optimal traffic routing and a theoretical upper bound on the network rate via a maximum-flow formulation, solvable with Ford-Fulkerson, yielding an optimized routing graph and upper bound . Stage Two transforms the problem into a penalty-based variant (V-RPTRMOP) and solves it with diffusion-model-enabled PSO (DM-PSO) to tune UAV placements and excitation weights so the actual rate approaches the upper bound. Simulations demonstrate significant rate improvements, routing superiority over classical protocols, and robustness to UAV position jitters and phase errors, highlighting practical potential for rapid disaster response. The approach provides a scalable, robust framework for cooperative UAV networks in emergency settings, balancing throughput, energy, and reliability.

Abstract

The low-altitude economy (LAE) plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communication. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. In this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP). Then, we propose a two-stage optimization approach to address it. In the first stage, the optimal traffic routing and the theoretical upper bound on the transmission rate of the network are derived. In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP, and a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm is proposed to deal with the V-RPTRMOP. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the constructed network, which demonstrates the great potential for post-disaster communications. Moreover, the robustness of the constructed network is also validated via evaluating the impact of two unexpected situations on the system transmission rate.
Paper Structure (33 sections, 7 theorems, 20 equations, 9 figures, 2 tables, 3 algorithms)

This paper contains 33 sections, 7 theorems, 20 equations, 9 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. UAV-assisted Post-disaster Communications
  4. Routing Design and UAV Network Optimization
  5. Solution Mechanisms
  6. System Model and Problem Formulation
  7. Network Architecture
  8. Communication Model
  9. Virtual Antenna Array Model
  10. Channel Model
  11. Transmission Model
  12. Energy Consumption Model
  13. Problem Formulation
  14. Stage One: Routing Design
  15. Derivation of Theoretical Upper Bound on the Transmission Rate of Each Communication Link
  16. ...and 18 more sections

Key Result

Lemma 1

Consider a collaborative system comprising $N_{\mathrm{U}}$ UAVs that form a VAA. Let $\rho$ denote the SNR achieved by a single UAV transmission. Then, the maximum achievable SNR of the VAA system, denoted as $\rho_{N_{\mathrm{U}}}$, is upper-bounded by: where the equality holds under ideal beamforming conditions.

Figures (9)

  • Figure 1: A UAV-swarm enabled collaborative self-organizing network for post-disaster communications.
  • Figure 2: An example illustrating the mapping of the association relationship among components in the network and the adjacency matrix. (a) A two-dimensional sketch map of a simple self-organizing network on the ground. (b) The adjacency matrix of the self-organizing network.
  • Figure 3: An example illustrating the Ford-Fulkerson algorithms for calculating the theoretical upper bound on the transmission rate of a network and the optimal traffic routing. (a) The original network, where node 0 is the transmitter and the node 4 is the receiver. (b) A residual network of (a), where an augmenting path 0$\rightarrow$1$\rightarrow$4 exists, thus the current upper bound on the transmission rate is 1. (c) A residual network of (a), where an augmenting path 0$\rightarrow$3$\rightarrow$4 exists, thus the current upper bound on the transmission rate is 2. (d) A residual network of (a) with no augmenting paths. (e) The final optimized network with an upper bound on the transmission rate of 2 and two corresponding paths.
  • Figure 4: The communication networks in different stages for case 1. (a) The communication network in the first stage after deducing the theoretical upper bound on the transmission rate of each link. (b) The optimized network in the first stage after conducting Ford-Fulkerson algorithm, with the theoretical upper bound on the transmission rate of the whole network is $4.48 \times 10^{6}$ bps. (c) The actual communication network in the second stage after UAV swarm optimization by DM-PSO, with the actual transmission rate of the whole network is $4.48 \times 10^{6}$ bps.
  • Figure 5: The communication networks in different stages for case 2. (a) The communication network in the first stage after deducing the theoretical upper bound on the transmission rate of each link. (b) The optimized network in the first stage after conducting Ford-Fulkerson algorithm, with the theoretical upper bound on the transmission rate of the whole network is $7.64 \times 10^{6}$ bps. (c) The actual communication network in the second stage after UAV swarm optimization by DM-PSO, with the actual transmission rate of the whole network is $7.64 \times 10^{6}$ bps.
  • ...and 4 more figures

Theorems & Definitions (11)

  • Lemma 1
  • Theorem 1
  • Theorem 2
  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Theorem 3
  • Theorem 4
  • Theorem 5
  • ...and 1 more