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UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks

Hongjuan Li, Hui Kang, Geng Sun, Jiahui Li, Jiacheng Wang, Xue Wang, Dusit Niyato, Victor C. M. Leung

TL;DR

This work tackles uplink interference from UAV-enabled data collection networks to terrestrial BSs in LoS-dominated A2G channels. It introduces a collaborative beamforming (CB) framework that forms a UAV-enabled virtual antenna array (VAA) to transmit data to multiple base stations, while jointly optimizing excitation weights, hover positions, and the transmission sequence via a multi-objective optimization problem (MOOP). A chaotic evolutionary algorithm, CNSGA-II, is proposed to solve the MOOP, featuring chaotic initialization, chaos-based crossover/mutation, and an elimination-based refinement, achieving better Pareto fronts than several baselines. Simulation results show substantial interference mitigation (approximately 4–5x improvement in total SINR) and improved data transmission efficiency and energy accounting, demonstrating the practical potential of CB-based UAV VAAs in data collection networks.

Abstract

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs.

UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks

TL;DR

This work tackles uplink interference from UAV-enabled data collection networks to terrestrial BSs in LoS-dominated A2G channels. It introduces a collaborative beamforming (CB) framework that forms a UAV-enabled virtual antenna array (VAA) to transmit data to multiple base stations, while jointly optimizing excitation weights, hover positions, and the transmission sequence via a multi-objective optimization problem (MOOP). A chaotic evolutionary algorithm, CNSGA-II, is proposed to solve the MOOP, featuring chaotic initialization, chaos-based crossover/mutation, and an elimination-based refinement, achieving better Pareto fronts than several baselines. Simulation results show substantial interference mitigation (approximately 4–5x improvement in total SINR) and improved data transmission efficiency and energy accounting, demonstrating the practical potential of CB-based UAV VAAs in data collection networks.

Abstract

Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. However, the line-of-sight dominance in air-to-ground communications often leads to significant interference with terrestrial networks, reducing communication efficiency among terrestrial terminals. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems. Specifically, the UAV swarm forms a UAV-enabled virtual antenna array (VAA) to achieve the transmissions of gathered data to multiple base stations (BSs) for data backup and distributed processing. However, there is a trade-off between the effectiveness of CB-based interference mitigation and the energy conservation of UAVs. Thus, by jointly optimizing the excitation current weights and hover position of UAVs as well as the sequence of data transmission to various BSs, we formulate an uplink interference mitigation multi-objective optimization problem (MOOP) to decrease interference affection, enhance transmission efficiency, and improve energy efficiency, simultaneously. In response to the computational demands of the formulated problem, we introduce an evolutionary computation method, namely chaotic non-dominated sorting genetic algorithm II (CNSGA-II) with multiple improved operators. The proposed CNSGA-II efficiently addresses the formulated MOOP, outperforming several other comparative algorithms, as evidenced by the outcomes of the simulations. Moreover, the proposed CB-based uplink interference mitigation approach can significantly reduce the interference caused by UAVs to non-receiving BSs.

Paper Structure

This paper contains 27 sections, 2 theorems, 21 equations, 11 figures, 4 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Interference Mitigation
  4. Collaborative Beamforming
  5. System Model
  6. Network Model
  7. Array Factor (AF) of UAV-enabled VAA
  8. Channel Model
  9. Transmission and Interference Model
  10. Energy Consumption Model
  11. Problem Formulation and Analysis
  12. The Proposed Method
  13. Traditional NSGA-II
  14. CNSGA-II
  15. Chaotic Solution Initialization Operator
  16. ...and 12 more sections

Key Result

Theorem 1

The formulated MOOP shown in Eq. (MOP-formulation) is NP-hard.

Figures (11)

  • Figure 1: Sketch map of a UAV-enabled VAA model for CB.
  • Figure 2: System model of an A2G transmission in the probabilistic LoS channel.
  • Figure 3: Sketch map of the population update.
  • Figure 4: The algorithm framework of CNSGA-II.
  • Figure 5: The main steps of deploying our method in practical systems.
  • ...and 6 more figures

Theorems & Definitions (2)

  • Theorem 1
  • Theorem 2