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Two-Way Aerial Secure Communications via Distributed Collaborative Beamforming under Eavesdropper Collusion

Jiahui Li, Geng Sun, Qingqing Wu, Shuang Liang, Pengfei Wang, Dusit Niyato

TL;DR

The paper addresses secure two-way UAV-to-UAV communications under eavesdropper collusion by using distributed collaborative beamforming (DCB) to shape signal distributions from UAV virtual antenna arrays. It formulates a NP-hard multi-objective optimization to maximize the known secrecy capacity $C_{KE}$, minimize the maximum sidelobe level (SLL), and reduce UAV energy, solved by the enhanced MOALO-RSI swarm intelligence method. The approach yields informative Pareto solutions that outperform several baselines and remains feasible on limited hardware, with robustness to channel and synchronization imperfections. The results demonstrate tangible improvements in security performance and energy efficiency for UAV swarms, highlighting practical deployment potential on inexpensive platforms like Raspberry Pi 4B. Overall, the work extends physical-layer security for aerial networks by integrating DCB with a specialized multi-objective optimization framework for colluding eavesdroppers.

Abstract

Unmanned aerial vehicles (UAVs)-enabled aerial communication provides a flexible, reliable, and cost-effective solution for a range of wireless applications. However, due to the high line-of-sight (LoS) probability, aerial communications between UAVs are vulnerable to eavesdropping attacks, particularly when multiple eavesdroppers collude. In this work, we aim to introduce distributed collaborative beamforming (DCB) into UAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two UAV swarms and construct these swarms as two UAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and the maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of UAVs for constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives as a multi-objective optimization problem. Following this, we propose an enhanced multi-objective swarm intelligence algorithm via the characterized properties of the problem. Simulation results show that our proposed algorithm can obtain a set of informative solutions and outperform other state-of-the-art baseline algorithms. Experimental tests demonstrate that our method can be deployed in limited computing power platforms of UAVs and is beneficial for saving computational resources.

Two-Way Aerial Secure Communications via Distributed Collaborative Beamforming under Eavesdropper Collusion

TL;DR

The paper addresses secure two-way UAV-to-UAV communications under eavesdropper collusion by using distributed collaborative beamforming (DCB) to shape signal distributions from UAV virtual antenna arrays. It formulates a NP-hard multi-objective optimization to maximize the known secrecy capacity , minimize the maximum sidelobe level (SLL), and reduce UAV energy, solved by the enhanced MOALO-RSI swarm intelligence method. The approach yields informative Pareto solutions that outperform several baselines and remains feasible on limited hardware, with robustness to channel and synchronization imperfections. The results demonstrate tangible improvements in security performance and energy efficiency for UAV swarms, highlighting practical deployment potential on inexpensive platforms like Raspberry Pi 4B. Overall, the work extends physical-layer security for aerial networks by integrating DCB with a specialized multi-objective optimization framework for colluding eavesdroppers.

Abstract

Unmanned aerial vehicles (UAVs)-enabled aerial communication provides a flexible, reliable, and cost-effective solution for a range of wireless applications. However, due to the high line-of-sight (LoS) probability, aerial communications between UAVs are vulnerable to eavesdropping attacks, particularly when multiple eavesdroppers collude. In this work, we aim to introduce distributed collaborative beamforming (DCB) into UAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. Specifically, we consider a two-way DCB-enabled aerial communication between two UAV swarms and construct these swarms as two UAV virtual antenna arrays. Then, we minimize the two-way known secrecy capacity and the maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers, respectively. Simultaneously, we also minimize the energy consumption of UAVs for constructing virtual antenna arrays. Due to the conflicting relationships between secure performance and energy efficiency, we consider these objectives as a multi-objective optimization problem. Following this, we propose an enhanced multi-objective swarm intelligence algorithm via the characterized properties of the problem. Simulation results show that our proposed algorithm can obtain a set of informative solutions and outperform other state-of-the-art baseline algorithms. Experimental tests demonstrate that our method can be deployed in limited computing power platforms of UAVs and is beneficial for saving computational resources.
Paper Structure (9 sections, 3 theorems, 20 equations, 5 figures, 2 tables, 2 algorithms)

This paper contains 9 sections, 3 theorems, 20 equations, 5 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Models and Preliminaries
  4. Problem Formulation and Analysis
  5. Algorithm Design
  6. Necessary Principles of Multi-objective Swarm intelligence
  7. Our Proposed MOALO-RSI Method
  8. Simulation Results
  9. Conclusion

Key Result

Lemma 1

The space around $\boldsymbol{P}^r$ is more informative since we are more likely to find positions of UAVs with low energy consumption and relatively high secure performance.

Figures (5)

  • Figure 1: A two-way DCB-enabled aerial communication system under the known and unknown eavesdropper collusion.
  • Figure 2: Visualization results obtained by our proposed MOALO-RSI.
  • Figure 3: Pareto solutions obtained by benchmarks and the proposed MOALO-RSI. All the nodes denote the objective values of the candidate solutions obtained by different algorithms.
  • Figure 4: Robustness verification results (in terms of $f_1$ and $f_2$) under some special cases.
  • Figure 5: An example of autonomous UAV systems based on Raspberry Pi 4B.

Theorems & Definitions (3)

  • Lemma 1
  • Lemma 2
  • Lemma 3