Access Control and Deployment Design for Multi-UAV Assisted Wireless Networks

Liu, Yaqun; Xie, Jun; Xing, Changyou; Xie, Shengxu

doi:10.1109/lwc.2022.3204060

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…The time complexity and signal overhead are

{\cal O}(T_{\text{epoch}}(C_2+T_{\text{br}}C_3))

and

{\cal O}(T_{\text{epoch}}(MN+T_{\text{br}}N))

, where

C_2

and

C_3

denote the constant complexity for best response and binary log‐learning method.

T_{\text{br}}

denotes the iteration number for best response method. BR‐CO [51]: The BR‐CO algorithm utilizes the BR method to update the user association. Each UAV updates the deployment scheme via convex optimization.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…method. T br denotes the iteration number for best response method.• BR-CO[51]: The BR-CO algorithm utilizes the BR method to update the user association. Each UAV updates the deployment scheme via convex optimization.The time complexity and signal overhead are (T epoch (C 2 + MN 3 𝜖)) and (T epoch (MN + T br N )), where 𝜖 is the accuracy tolerance.…”

mentioning

confidence: 99%

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Joint optimization of deployment, user association, channel, and resource allocation for fairness‐aware multi‐UAV network

Sun,

Wang,

Qin

et al. 2024

IET Communications

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This paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle‐enabled access network. Since different user equipments performing different tasks and have different data rate requirements, the priority‐based traffic fairness problem is investigated. This problem, however, is a mixed integer nonlinear programming problem with NP‐hard complexity, making it challenging to be solved. To address this issue, a self‐organized and distributed framework “sense‐as‐you‐fly” based on the decomposition process, which divides the original problem into several subproblems, is proposed. Assuming without central controller, we derive the closed‐form resource allocation scheme and propose distributed many‐to‐one matching to optimize user association subproblem. Considering the coupled characteristics, the multi‐unmanned aerial vehicle deployment and channel allocation subproblems are modelled as a local altruistic game. The existence of Nash equilibrium is proved with the aid of exact potential game and efficient best response learning‐based algorithm is proposed. The original problem is finally addressed by solving the sub‐problems alternately and iteratively. Simulation results verify its effectiveness. By jointly optimizing multidimensional variables, the proposed algorithm unlocks network performance gains, especially in resource‐limited regimes.

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“…The time complexity and signal overhead are

{\cal O}(T_{\text{epoch}}(C_2+T_{\text{br}}C_3))

and

{\cal O}(T_{\text{epoch}}(MN+T_{\text{br}}N))

, where

C_2

and

C_3

denote the constant complexity for best response and binary log‐learning method.

T_{\text{br}}

Section: Simulation Results and Analysismentioning

confidence: 99%

mentioning

confidence: 99%

Joint optimization of deployment, user association, channel, and resource allocation for fairness‐aware multi‐UAV network

Sun,

Wang,

Qin

et al. 2024

IET Communications

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show abstract

“…of UAV-BSs deployed, but lacks deployment flexibility as the altitude and service range of UAV-BSs are immutable. In [6], the authors modeled the access control of GUs and UAV-BS deployment problem as a potential game and a convex optimization problem, respectively. They propose a method to examine a limited number of user association samples and then select the sample with the best response (or reward) as each ground user's access decision.…”

mentioning

confidence: 99%

Interference-Aware Deployment for Maximizing User Satisfaction in Multi-UAV Wireless Networks

Lai

Tsai

Ting

et al. 2023

IEEE Wireless Commun. Lett.

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In this letter, we study the deployment of Unmanned Aerial Vehicle mounted Base Stations (UAV-BSs) in multi-UAV cellular networks. We model the multi-UAV deployment problem as a user satisfaction maximization problem, that is, maximizing the proportion of served ground users (GUs) that meet a given minimum data rate requirement. We propose an interferenceaware deployment (IAD) algorithm for serving arbitrarily distributed outdoor GUs. The proposed algorithm can alleviate the problem of overlapping coverage between adjacent UAV-BSs to minimize inter-cell interference. Therefore, reducing co-channel interference between UAV-BSs will improve user satisfaction and ensure that most GUs can achieve the minimum data rate requirement. Simulation results show that our proposed IAD outperforms comparative methods by more than 10% in user satisfaction in high-density environments.

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A Safety System for Maximizing Operated UAVs Capacity Under Regulation Constraints

Ahmed,

Sheltami

2023

IEEE Access

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Recently, the emergence of Unmanned Aerial Vehicles (UAVs) has garnered significant attention due to their widespread applications, such as surveillance, mapping, reconnaissance, as well as commercial delivery, and photography. Despite the tremendous applications of UAVs, there are potential risks associated with drones that may impact flight safety. For instance, launching and releasing drones near airfields can pose serious threats to flight safety. Another challenge is the flying altitude. Flying at high altitudes might cause a collision with other aircraft, and flying at low altitudes can also pose a significant threat due to obstacles in the environment. Various regulations, such as airspace restrictions, flight altitude limits, and safety requirements, can limit the number of UAVs operating in a particular area. This, in turn, makes it challenging to specify the number of UAVs that can operate safely. To address these challenges, we propose an optimization strategy to maximize the number of UAVs that can operate while adhering to regulatory constraints. The problem is formulated and solved using an improved version of a populationbased meta-heuristic, IPSO. In the proposed approach, we consider two distinct objective functions. The first one is the local objective function, which aims to minimize the energy consumption of the generated path by IPSO. This objective function is crucial in ensuring that the generated path is energy-efficient. The second objective function is the global objective function of the proposed approach, and aims to maximize the number of UAVs that can operate in a specific area. The proposed approach studies the impact of regulations such as obstacles and flying altitude on a region capacity. The results show that the proposed approach can increase the the region capacity, i.e., number of UAVs, to the maximum possible while ensuring safety and regulatory constraints.

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Access Control and Deployment Design for Multi-UAV Assisted Wireless Networks

Cited by 8 publications

References 8 publications

Joint optimization of deployment, user association, channel, and resource allocation for fairness‐aware multi‐UAV network

Joint optimization of deployment, user association, channel, and resource allocation for fairness‐aware multi‐UAV network

Interference-Aware Deployment for Maximizing User Satisfaction in Multi-UAV Wireless Networks

A Safety System for Maximizing Operated UAVs Capacity Under Regulation Constraints

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