Covert Multicast in UAV-Enabled Wireless Communication Systems With One-hop and Two-hop Strategies

• URL: http://arxiv.org/abs/2410.12287v1
• Date: Wed, 16 Oct 2024 06:46:30 GMT
• Title: Covert Multicast in UAV-Enabled Wireless Communication Systems With One-hop and Two-hop Strategies
• Authors: Wenhao Zhang, Ji He, Yuanyu Zhang,
• Abstract summary: 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.
• Score: 8.702721247072429
• License:
• Abstract: This paper delves into the time-efficient covert multicast in a wireless communication system facilitated by Unmanned Aerial Vehicle (UAV), in which the UAV aims to disseminate a common covert information to multiple ground users (GUs) while suffering from the risk of detection by a ground warden (Willie). We propose one hop (OH) and two hop (TH) transmission schemes, first develop a theoretical framework for performance modeling of both the detection error probability at Willie and the transmission time at UAV. The optimization problems subject to the covertness constraint for the two transmission schemes are then formulated to gain insights into the system settings of the UAV's prior transmit probability, transmit power and horizontal location that affect the minimum transmission time. The optimization problems are non-convex and challenging to give numerical results. We thus explore the optimal setting of the transmit power and the prior transmit probability for the UAV separately under specific parameters with two schemes. We further propose a particle swarm optimization (PSO) based algorithm and an exhaustive algorithm to provide the joint solutions for the optimization problem with the OH transmission scheme and TH scheme, respectively. Finally, the efficiency of the proposed PSO-based algorithm is substantiated through extensive numerical results.

Related papers

• Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless Communication Systems [16.359317035378638]
  Grant-free transmission and cell-free communication are vital in improving coverage and quality-of-service for massive machine-type communication. This paper proposes a novel framework of joint active user detection, channel estimation, and data detection (JACD) for massive grant-free transmission in cell-free wireless communication systems.
  arXiv  Detail & Related papers  (2025-02-19T03:04:10Z)
• UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks [71.23793087286703]
  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.
  arXiv  Detail & Related papers  (2024-12-09T12:56:50Z)
• 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)
• Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning [52.7230652428711]
  Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
  arXiv  Detail & Related papers  (2023-06-05T16:01:33Z)
• Energy-Efficient Cellular-Connected UAV Swarm Control Optimization [25.299881367750487]
  We propose a two-phase command and control (C&C) transmission scheme in a cellular-connected UAV swarm network. We formulate the problem as a Constrained Markov Decision Process to find the optimal policy. Our algorithm could maximize the number of UAVs that successfully receive the common C&C under energy constraints.
  arXiv  Detail & Related papers  (2023-03-18T11:42:04Z)
• Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System [25.32139119893323]
  Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things (IoT) systems. The UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. This article aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV.
  arXiv  Detail & Related papers  (2022-10-27T06:27:40Z)
• 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.