PSO‐Based UAV Deployment and Dynamic Power Allocation for UAV‐Enabled Uplink NOMA Network

Abdel-Razeq, Sharief; Shakhatreh, Hazim; Alenezi, Ali; Sawalmeh, Ahmad; Anan, Muhammad; Almutiry, Muhannad

doi:10.1155/2021/2722887

Cited by 11 publications

(4 citation statements)

References 54 publications

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“…Step 1 (2) for i � 1 to Ndo (3) Create a list of UAVs that satisfy A 4 constraint (4) end for (5) Step 2 (6) for j � 1 to Mdo (7) ifScenario 1then (8) choose users having maximum data rate (i.e., max. r ij ) ( 9) end (10) else ifScenario 2then (11) choose users having minimum data rate (i.e., min.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…In these scenarios, UAVs as base stations (BSs) have become a promising solution in future wireless networks. UAV-BSs can help a terrestrial BS network provide high data rate coverage at any time and place it is required [5][6][7]. Also, UAV-BSs can deal with the issue of temporary coverage in rural and poorly populated areas and when terrestrial wireless infrastructure has been harmed by a natural catastrophe [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Efficient 3D Positioning of UAVs and User Association Based on Hybrid PSO-K-Means Clustering Algorithm in Future Wireless Networks

Shakhatreh

Ababneh

2023

Mobile Information Systems

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Unmanned aerial vehicles (UAVs) play an important role in the future of 5G and 6G communication networks. UAV-assisted communication offers the benefits of improved network capacity and coverage. A typical communication setup is for UAVs to connect users to the core network via a backhaul channel. Some of the challenges in such a setup include user-UAV association and management of the backhaul channel. These two challenges are greatly impacted by the positioning of the UAVs in the network. In this article, we address these challenges by considering a joint UAV placement and user association problem under data rate, signal to interference and noise ratio, and bandwidth constraints. To overcome this problem, a hybrid PSO-K-means clustering algorithm is used in two stages. In the first stage, we use a K-means algorithm to cluster users and determine their horizontal locations. In the second stage, we use particle swarm optimization (PSO) to find the efficient 3D position of UAVs to maximize various network designs, namely, the network-centric approach and the user-centric approach. The performance of the proposed solution is verified using simulation results.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient 3D Positioning of UAVs and User Association Based on Hybrid PSO-K-Means Clustering Algorithm in Future Wireless Networks

Shakhatreh

Ababneh

2023

Mobile Information Systems

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“…The worstcase complexity of SCPMax/SOPMin-PSO is O (IN X). Notably, PSO is simpler and faster than ant colony, genetic, or artificial bee colony algorithms [65].…”

Section: Optimization: Problem Formulation and Solutionmentioning

confidence: 99%

Secrecy Performance Analysis and Optimization for UAV-Relay-Enabled WPT and Cooperative NOMA MEC in IoT Networks

Nguyen,

Ha,

Truong

et al. 2023

IEEE Access

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This paper investigates a system for unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) and non-orthogonal multiple access (NOMA) mobile edge computing (MEC) in the Internet of Things (IoT) UWNMI, wherein a UAV equipped with an energy transmitter (ET) acts as a relay. In particular, we consider a situation involving two clusters of IoT devices (IDs) that have limited resources and therefore are unable to compute their own tasks and must instead offload them to a base station (BS) via the UAV relay (UR) in the presence of a passive eavesdropper. In the UWNMI system, the effects of imperfect channel state information (ICSI) and imperfect successive interference cancellation (ISIC) are considered, along with the usage of artificial noise (AN) to improve the physical layer security (PLS) of the system. We derive closed-form expressions for the successful computation probability (SCP) and secrecy outage probability (SOP) under the Nakagami-m fading channel model in order to evaluate the performance of the system. Moreover, we present optimization problems with the objectives of optimizing the position and height of the UR, the time switching ratio (TSR), and the power allocation for the AN in order to maximize the SCP and minimize the SOP. These problems are solved using a particle swarm optimization (PSO)based algorithm. In addition, Monte Carlo simulation results are presented to validate the accuracy of our analysis based on simulations of the system performance under different values of the system parameters, including the number of antennas at the BS, the number of IDs in each cluster, the TSR, and the position and height of the UR.INDEX TERMS Internet of Things, unmanned aerial vehicles, energy harvesting, wireless power transfer, non-orthogonal multiple access, mobile edge computing, physical layer security. I. INTRODUCTIONRecent developments in Internet of Things (IoT) technology have inspired a multitude of unique applications, including intelligent grazing, autonomous control, and environmental monitoring [1]- [4]. For an IoT system to collect environmental data, many IoT devices (IDs) must be dis-tributed. Typically, these IDs are powered by limited-capacity batteries that must be routinely updated or recharged. Radio frequency (RF)-signal-based wireless power transfer (WPT), also known as RF energy harvesting (EH), has been widely acknowledged as a promising solution for powering lowpower IDs autonomously, thereby eliminating the need to

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“…In NOMA networks, users are often divided into two categories, weak and strong users. Weak users are the users with low channel gain, such users usually the ones that are located faraway from the BS, whereas strong users are quite the opposite, i.e., the users that have high channel gain and usually located near the BS [19], [20]. Furthermore, grouping users is a useful strategy for reducing system complexity [21].…”

Section: Noma Pairingmentioning

confidence: 99%

Mobile-IRS Assisted Next Generation UAV Communication Networks

Shakhatreh¹,

Sawalmeh²,

Alenezi³

et al. 2022

Preprint

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Prior research on intelligent reflection surface (IRS)assisted unmanned aerial vehicle (UAV) communications has focused on a fixed location for the IRS or mounted on a UAV.The assumption that the IRS is located at a fixed position will prohibit mobile users from maximizing many wireless network benefits, such as data rate and coverage. Furthermore, assuming that the IRS is placed on a UAV is impractical for various reasons, including the IRS's weight and size and the speed of wind in severe weather. Unlike previous studies, this study assumes a single UAV and an IRS mounted on a mobile ground vehicle (M-IRS) to be deployed in an Internet-of-Things (IoT) 6G wireless network to maximize the average data rate. Such a methodology for providing wireless coverage using an M-IRS assisted UAV system is expected in smart cities. In this paper, we formulate an optimization problem to find an efficient trajectory for the UAV, an efficient path for the M-IRS, and users' power allocation coefficients that maximize the average data rate for mobile ground users. Due to its intractability, we propose efficient techniques that can help in finding the solution to the optimization problem. First, we show that our dynamic power allocation technique outperforms the fixed power allocation technique in terms of network average sum rate. Then we employ the individual movement model (Random Waypoint Model) in order to represent the users' movements inside the coverage area. Finally, we propose an efficient approach using a Genetic Algorithm (GA) for finding an efficient trajectory for the UAV, and an efficient path for the M-IRS to provide wireless connectivity for mobile users during their movement. We demonstrate through simulations that our methodology can enhance the average data rate by 15% on average compared with the static IRS and by 25% on average compared without the IRS system.

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PSO‐Based UAV Deployment and Dynamic Power Allocation for UAV‐Enabled Uplink NOMA Network

Cited by 11 publications

References 54 publications

Efficient 3D Positioning of UAVs and User Association Based on Hybrid PSO-K-Means Clustering Algorithm in Future Wireless Networks

Efficient 3D Positioning of UAVs and User Association Based on Hybrid PSO-K-Means Clustering Algorithm in Future Wireless Networks

Secrecy Performance Analysis and Optimization for UAV-Relay-Enabled WPT and Cooperative NOMA MEC in IoT Networks

Mobile-IRS Assisted Next Generation UAV Communication Networks

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