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This paper introduces collaborative beamforming (CB) for unmanned aerial vehicle (UAV)-assisted networks to enhance data transmission rates while minimizing energy expenditure. By forming a virtual element antenna array (VEAA), multiple UAVs cooperatively transmit data in synchronization, leveraging a high-gain mainlobe (ML) beam for efficient signal propagation. The study focuses on optimizing two key aspects: UAV placement within the VEAA, and excitation current weight allocation for CB transmission, both constrained by energy consumption during UAV deployment. We formulate this challenge as a Multi-Objective Energy-Aware Communication Optimization Problem (MECOP), aiming to Maximize transmission rate, Minimize peak sidelobe level (SLL), and Reduce UAV energy usage. To solve MECOP, we propose an Hybrid Ant Lion Optimizer (HALO) to improve convergence. Simulation results validate that HALO significantly improves energy efficiency in UAV-assisted CB networks compared to conventional methods.

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Energy Efficient UAV-Assisted Communications Based on Collaborative Beamforming: A Multi-objective Optimization Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Tingting Zheng,
  • Xin Feng,
  • Jing Zhang,
  • Xinrong Guo,
  • Qiuyan Chen

摘要

This paper introduces collaborative beamforming (CB) for unmanned aerial vehicle (UAV)-assisted networks to enhance data transmission rates while minimizing energy expenditure. By forming a virtual element antenna array (VEAA), multiple UAVs cooperatively transmit data in synchronization, leveraging a high-gain mainlobe (ML) beam for efficient signal propagation. The study focuses on optimizing two key aspects: UAV placement within the VEAA, and excitation current weight allocation for CB transmission, both constrained by energy consumption during UAV deployment. We formulate this challenge as a Multi-Objective Energy-Aware Communication Optimization Problem (MECOP), aiming to Maximize transmission rate, Minimize peak sidelobe level (SLL), and Reduce UAV energy usage. To solve MECOP, we propose an Hybrid Ant Lion Optimizer (HALO) to improve convergence. Simulation results validate that HALO significantly improves energy efficiency in UAV-assisted CB networks compared to conventional methods.