UAV-Assisted Multi-path Parallel Routing for mmWave-Based Wireless Networks

Particle Swarm Optimization (PSO) has been widely employed to optimize the deployment of Unmanned Aerial Vehicles (UAVs) in various scenarios, particularly because of its efficiency in handling both single and multi-objective optimization problems. In this paper, a framework for optimizing the deployment of edge-enabled UAVs using Pareto-PSO is proposed for data collection scenarios in which UAVs operate autonomously and execute onboard distributed multi-objective PSO to maximize the total non-overlapping coverage area while minimizing latency and energy consumption. Performance evaluation is conducted using key indicators, including convergence time, throughput, and total non-overlapping coverage area across bandwidth and swarm-size sweeps. Simulation results demonstrate that the Pareto-PSO consistently attains the highest throughput and the largest coverage envelope, while exhibiting moderate and scalable convergence times. These results highlight the advantage of treating the objectives as a vector-valued objective in Pareto-PSO for real-time, scalable, and energy-aware edge-UAV deployment in dynamic Internet of Flying Things environments.

In remote areas, natural disasters owing to extreme weather or other causes can easily spread regionally. Unmanned aerial vehicles (UAVs) are regarded as pioneers for exploration and sensing in post-disaster scenarios due to their characteristics of flexible deployment and low cost. Considering the network disconnection caused by the long distances between the sensing area, UAVs, and the ground station (GS), this article aims to maximize the spatial exploration ratio (SER) while minimizing energy consumption with connectivity maintenance. To ensure that the UAV swarm successfully transmits as much post-disaster data as possible back, we develop a worst-case soft actor critic (WCSAC) based deployment algorithm with role management for UAVs. First, we formulate the three-dimensional (3D) UAV deployment problem as a real-time single-step Markov decision process (CMDP), together with role management that includes role assignment and role switching for each UAV. Next, to avoid unlimited energy consumption for complete coverage of remote mission areas, we separate the reward and cost functions and translate the energy consumption minimization to the residual energy constraints with a conditional value-at-risk (CVaR). Finally, visualizations and numerical results prove that the proposed WCSAC-cntRM can strike a better trade-off among the valid SER, energy consumption, and network connectivity than other benchmarks, especially when the emergency mission is dynamically triggered in the corner area.

In order to fully utilize the spectrum resources, this work considers a unmanned aerial vehicle (UAV) uplink communication system based on non-orthogonal multiple access technology (NOMA), in which the UAV receives information from the ground users with a certain flying altitude. As an initial study, we consider a simplified setup with two ground users to draw some insightful results. Explicitly, we first formulate an optimization problem that maximizes the sum throughput subject to each user's transmit power constraint and their corresponding minimum transmission rate requirement. Then, both the optimal transmit power and UAV's deployment location are derived with the aid of employing the Karush-Kuhn-Tucher (KKT) conditions. Simulation results show that the proposed UAV's deployment scheme with the users' power allocation can achieve a higher sum throughput compared with two existing benchmark schemes.

Unmanned aerial vehicles (UAVs) are playing an important role in serving ground users as base stations (BSs) during temporary events, or after terrestrial BSs fail. UAV deployment is a fundamental and vital problem which directly affects the coverage capacity and user experience. Especially, while deploying a UAV backbone network, the cost and the quality of service (QoS) of the ground users as well as the connectivity among UAVs need to be considered jointly. In this paper, the deployment problem is modeled as minimizing the amount of deployed UAVs while guaranteeing the coverage capacity and connectivity among UAVs. Further, a novel UAV deployment scheme is proposed where a heuristic algorithm is adopted to delete locations from the initially defined candidate UAV locations iteratively, and UAVs are deployed at the remaining locations finally. The simulation results show that our UAV deployment scheme can achieve minimum deployed UAVs while ensuring QoS requirements and a robust network among UAVs.

In this paper, a power efficient deployment for visible light communication (VLC)-enabled unmanned aerial vehicles (UAVs) is studied. In the studied model, each UAV can provide communication service for ground users and illumination builds the VLC links between UAVs and users. Hence, each UAV’s signal transmission and illumination will affect other UAVs’ signal transmission and illumination. Therefore, to deploy VLC-enabled UAVs so as to efficiently service the ground users, the interference caused by the signal transmission and illumination of UAVs must be considered. This problem is formulated as an optimization problem whose goal is to optimize the deployment of UAVs so as to minimize the power consumption for signal transmission and illumination. An iterative algorithm is first proposed to transform the optimization problem into a series of interdependent subproblems, and the transformed problems are then solved by the Lagrangian dual method. In addition, convergence and complexity of the algorithm are also analyzed. Numerical results show that the proposed scheme can reduce at least 53.7% power consumption when compared to the baselines with UAVs at the center of each sub-area.

This article establishes a new multiunmanned aerial vehicle (multi-UAV)-enabled mobile edge computing (MEC) system, where a number of unmanned aerial vehicles (UAVs) are deployed as flying edge clouds for large-scale mobile users. In this system, we need to optimize the deployment of UAVs, by considering their number and locations. At the same time, to provide good services for all mobile users, it is necessary to optimize task scheduling. Specifically, for each mobile user, we need to determine whether its task is executed locally or on a UAV (i.e., offloading decision), and how many resources should be allocated (i.e., resource allocation). This article presents a two-layer optimization method for jointly optimizing the deployment of UAVs and task scheduling, with the aim of minimizing system energy consumption. By analyzing this system, we obtain the following property: the number of UAVs should be as small as possible under the condition that all tasks can be completed. Based on this property, in the upper layer, we propose a differential evolution algorithm with an elimination operator to optimize the deployment of UAVs, in which each individual represents a UAV's location and the entire population represents an entire deployment of UAVs. During the evolution, we first determine the maximum number of UAVs. Subsequently, the elimination operator gradually reduces the number of UAVs until at least one task cannot be executed under delay constraints. This process achieves an adaptive adjustment of the number of UAVs. In the lower layer, based on the given deployment of UAVs, we transform the task scheduling into a 0-1 integer programming problem. Due to the large-scale characteristic of this 0-1 integer programming problem, we propose an efficient greedy algorithm to obtain the near-optimal solution with much less time. The effectiveness of the proposed two-layer optimization method and the established multi-UAV-enabled MEC system is demonstrated on ten instances with up to 1000 mobile users.

Deploying low-altitude unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) is a promising approach to enhance cellular network coverage in dynamic and infrastructure-constrained environments. However, real-world UAV deployment involves complex challenges, including managing partially observable and mobile users, maintaining multi-hop backhaul connectivity to a fixed ground base station, mitigating co-channel interference via minimum UAV separation, and optimizing coverage efficiency, fairness, and energy consumption. This paper proposes a multi-agent deep reinforcement learning (MARL) framework that formulates the dynamic UAV deployment task as a centralized multi-input multi-output policy. Our method employs sequential action masking to guarantee multi-hop connectivity and enforce minimum distance constraints during UAV placement. The policy network integrates graph neural networks, residual convolutional layers, and feature-wise linear modulation to effectively capture spatial relations, inter-UAV interactions, and global and local network states. We validate our approach through extensive simulations across various user density scenarios, demonstrating significant improvements in coverage efficiency, fairness of coverage distribution, and flight energy reduction compared to strong baseline algorithms. Our proposed framework provides a comprehensive solution that simultaneously addresses dynamic user mobility, multi-hop connectivity, interference mitigation, and energy efficiency in UAV-assisted cellular networks.

The massive deployment of unmanned aerial vehicles (UAVs) is growing in various fields in wireless network of 5G and 6G. It supports huge deployment of UAVs that offer secure, reliable, and low-cost data communications. They are widely deployed in surveillance and military applications for collecting sensitive and critical data, transferring the collected data to the data center, procuring high-definition images of disaster areas, surveying natural disaster zones, and other critical missions. Although, the UAV applications are growing, the risk and security factors associated with it are increasing parallelly. So, a need of secure and reliable network communication system for UAV is mandatory. In this chapter, the performance of UAV network system in 6G environment and the technological challenges faced with respect to security and privacy are discussed. Then a possible solution using blockchain and security policies that can be provided for a secure communication in a 6G network is presented.

A survey on UAV-assisted wireless communications: Recent advances and future trends

PurposeThe purpose of the paper is to describe the evolving regulatory structures of the civilian unmanned aerial vehicle (UAV) in India and Japan, not yet fully developed to regulate the deployment of the UAV. India and Japan are at the forefront to overhaul the respective regulatory framework to address issues of accountability, responsibility and risks associated with the deployment of UAV technologies.Design/methodology/approachIn-depth interviews are conducted both in Japan and India to gather primary data based on the snowball sampling method. The paper addresses questions such as what is the current scenario of civilian UAV deployment in India and Japan. What are the regulation structures for Civil UAV deployment and operation and how they differ in India and Japan? What are the key regulatory challenges for Civil UAV deployment in India? How regulation structure enables or inhibits the users and operators of Civil UAVs in India? What are mutual learnings concerning UAV regulations?FindingsFindings reveal that the Indian regulations address issues of responsibility by imparting values of privacy, safety, autonomy and security; Japanese regulation prefers values of trust, responsibility, safety and ownership with more freedom to experiment.Originality/valueThe study on civilian UAV regulatory framework is a new and innovative work embedded by the dimensions of responsibility and accountability from a responsible innovation perspective. The work is a new contribution to innovation literature looked at from regulatory structures. Field visits to both Japan and India enrich the study to a new elevation.

Deployment of unmanned aerial vehicles (UAVs) is recently getting significant attention due to a variety of practical use cases, such as surveillance, data gathering, and commodity delivery. Since UAVs are powered by batteries, energy efficient communication is of paramount importance. In this paper, we investigate the problem of lifetime maximization of a UAV-assisted network in the presence of multiple sources of interference, where the UAVs are deployed to collect data from a set of wireless sensors. We demonstrate that the placement of the UAVs play a key role in prolonging the lifetime of the network since the required transmission powers of the UAVs are closely related to their locations in space. In the proposed scenario, the UAVs transmit the gathered data to a primary UAV called leader, which is in charge of forwarding the data to the base station (BS) via a backhaul UAV network. We deploy tools from spectral graph theory to tackle the problem due to its high non-convexity. Simulation results demonstrate that our proposed method can significantly improve the lifetime of the UAV network.

Unmanned Aerial Vehicles (UAVs) have emerged as crucial components of 5G Internet of Things (IoT) networks, expanding coverage and capacity in various applications. While prior research has extensively examined the impact of power control factor adjustments on UAV uplink transmission, limited attention has been devoted to UAV downlink transmission, which is vital for seamless connectivity and quality of service. In this study, we address this research gap by investigating the effects of reduced power control factors on UAV downlink transmission. We use MATLAB simulations to model how user equipment (UE) is randomly placed in a UAV network. We then check the coverage probability and spectral efficiency for UAV altitudes between 200 and 500 m, taking into account different power control factors in the downlink transmission. Our findings show that a certain threshold of 0.05 for the power control factor keeps coverage probabilities at different heights, making sure that served UEs have enough signal strength. Surprisingly, there is no significant change in spectral efficiency when compared to the baseline results. This means that lowering the power control factor does not hurt spectral efficiency in UAV downlink transmission. These results help us figure out how to make UAV communication work better in 5G IoT networks. They also help us make smart choices about how to change the power to make up for lost downlink signals while keeping the quality of service high. Moreover, our study lays the groundwork for future research in UAV deployment and communication optimization within advanced wireless networks.

The contemporary deployment of unmanned aerial vehicles (UAVs) for commercial purposes and electromagnetic pulse (EMP) threat to mechatronic devices necessitates the study of EMP on surveillance UAVs. This research paper describes the effect of EMP interference on the structure, communication and cabling circuit of the UAV. This was achieved using the CST microwave studio with an induced plane wave referring to MIL-STD464 and applying the transmission line matric solver method. The cable field coupling for bi-directional, uni-directional radiation and uni-directional irradiation were carried out with a constant resistive load of 50 Ω and the induced coupling currents and voltages, current in space domain, TLM mesh is obtained indicating the susceptibility of the UAV under different conditions of EMP interference and the electromagnetic distribution in the UAV circuit layout. Experimentally, the UAV is setup with an EMP generator and its response was observed at 12 m, 10 m and 8 m from the radiation source with the corresponding field strengths.

Unmanned Aerial Vehicles (UAVs) have been utilized to serve on-ground users with various services, e.g., computing, communication and caching, due to their mobility and flexibility. The main focus of many recent studies on UAVs is to deploy a set of homogeneous UAVs with identical capabilities controlled by one UAV owner/company to provide services. However, little attention has been paid to the issue of how to enable different UAV owners to provide services with differentiated service capabilities in a shared area. To address this issue, we propose a multi-agent imitation learning enabled UAV deployment approach to maximize both profits of UAV owners and utilities of on-ground users. Specially, a Markov game is formulated among UAV owners and we prove that a Nash equilibrium exists based on the full knowledge of the system. For online scheduling with incomplete information, we design agent policies by imitating the behaviors of corresponding experts. A novel neural network model, integrating convolutional neural networks, generative adversarial networks and a gradient-based policy, can be trained and executed in a fully decentralized manner with a guaranteed <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -Nash equilibrium. Performance results show that our algorithm has significant superiority in terms of average profits, utilities and execution time compared with other representative algorithms.

The flexible and easy-to-deploy Unmanned Aerial Vehicle(UAV) is used to control the manned-aircraft formations with highly dynamic topology changes, forming a manned and unmanned cooperative airborne network, which can effectively reduce network deployment costs while ensuring network connectivity. Aiming at the problem of UAV deployment, in order to optimize network reliability and deployment cost overhead indicators, a UAV deployment algorithm based on connectivity and cost minimization is proposed. Firstly, in order to achieve full coverage of the mission area to ensure connectivity, preliminary deployment is performed based on the communication range of the UAV. Then, based on deployment constraints and optimization indicators, the redundant UAVs in the preliminary deployment are determined and deleted. The experimental results show the effectiveness of the proposed algorithm.