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

• URL: http://arxiv.org/abs/2412.06456v1
• Date: Mon, 09 Dec 2024 12:56:50 GMT
• Title: UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks
• Authors: Hongjuan Li, Hui Kang, Geng Sun, Jiahui Li, Jiacheng Wang, Xue Wang, Dusit Niyato, Victor C. M. Leung,
• Abstract summary: Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems.
• Score: 71.23793087286703
• License:
• 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.

Related papers

• AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT [32.6091251316091]
  This paper proposes a UAV-assisted system based on distributed beamforming to enhance age forwarding information (AoI) in Internet of Things (IoT) We propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing stability and accelerate convergence.
  arXiv  Detail & Related papers  (2025-02-13T07:48:36Z)
• Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning [59.660724802286865]
  Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. This work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users.
  arXiv  Detail & Related papers  (2025-02-09T09:15:47Z)
• Covert Multicast in UAV-Enabled Wireless Communication Systems With One-hop and Two-hop Strategies [8.702721247072429]
  We study the time of covert multicast in a wireless communication system facilitated by Unmanned Aerial Vehicle (UAV) We propose one (OH) particle swarm (PSO) based algorithm and an exhaustive framework for performance modeling for the transmission scheme and TH scheme, respectively. The efficiency of the proposed PSO-based algorithm is substantiated through extensive numerical results.
  arXiv  Detail & Related papers  (2024-10-16T06:46:30Z)
• UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
  We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs. We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
  arXiv  Detail & Related papers  (2024-04-11T03:19:22Z)
• UAV Swarm-enabled Collaborative Secure Relay Communications with Time-domain Colluding Eavesdropper [115.56455278813756]
  Unmanned aerial vehicles (UAV) as aerial relays are practically appealing for assisting Internet Things (IoT) network. In this work, we aim to utilize the UAV to assist secure communication between the UAV base station and terminal terminal devices.
  arXiv  Detail & Related papers  (2023-10-03T11:47:01Z)
• Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays [55.736718475856726]
  Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT) Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points. We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
  arXiv  Detail & Related papers  (2023-08-03T02:49:50Z)
• Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraints [0.0]
  Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks. In this paper, we reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints. We propose a decentralized deep reinforcement learning approach to solve the problem.
  arXiv  Detail & Related papers  (2021-04-03T22:22:20Z)
• Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms [98.78553146823829]
  Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks. In this paper, a novel framework is proposed to implement distributed learning (FL) algorithms within a UAV swarm.
  arXiv  Detail & Related papers  (2020-02-19T14:04:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.