Joint mission planning and spectrum resources optimization for multi‐UAV reconnaissance

Liao, Naiwen; He, Panfeng; Du, Yihang; Zhang, Yu; Chen, Yong; Liang, Tao

doi:10.1049/cmu2.12541

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…In addition, in Reference 34, the authors formulate the best resource allocations for UAV and macro‐BS (MBS) to find the optimal location of UAVs. To increase the pace of information transmission by reusing the spectrum of current users, a joint mission planning and spectrum resource optimization algorithm for several unmanned aerial vehicles is presented in Reference 35. Sawalmeh et al proposed a technique involving multi‐UAV 3D deployment with power‐efficient planning with the aim of reducing the number of UAVs used to provide wireless coverage to all outdoor and indoor users while minimizing the required UAV transmit power and satisfying users' required data rates 36 .…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient three‐dimensional deployment of multiple unmanned aerial vehicles supporting ground base station toward maximizing served users with heterogeneous quality‐of‐service requirements

Hayajneh,

Abualkishik,

Bani‐Hani

et al. 2023

Trans Emerging Tel Tech

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SummaryUnmanned aerial vehicles (UAVs) are critical in wireless networks that can provide wireless broadcast and high‐rate transmissions. UAVs can be used as flying base stations without an infrastructure to improve coverage, capacity, line‐of‐sight (LoS) connection, and rate performance in wireless communication. Furthermore, UAVs support terrestrial base stations in overload demand and disaster situations. Our presented method enables the ground base station (GBS) to collaborate with many UAVs while optimizing the number of served users and considering the influence of obstructing barriers on the air‐to‐ground (A2G) model. Our work investigates a model to find the efficient placements of multiple UAVs and optimize bandwidth between UAVs and GBS to enhance network performance by maximizing the number of served users with heterogeneous quality‐of‐service requirements. Three approaches have been employed to find the minimal number of UAVs that maximize the number of served users in the given area. The first is hybrid exhaustive search and K‐means algorithms, the second is hybrid particle swarm optimization (PSO) and K‐means algorithms, and the third is the greedy algorithm. The simulation results show that the maximum percentage of users served is equal to 100% using the exhaustive search algorithm. Furthermore, the percentage once using the PSO algorithm is 98% and 99.5% when deploying three and four UAVs, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Efficient three‐dimensional deployment of multiple unmanned aerial vehicles supporting ground base station toward maximizing served users with heterogeneous quality‐of‐service requirements

Hayajneh,

Abualkishik,

Bani‐Hani

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…Considering the limited spectral resources and energy constraints of IoT devices, fixed terrestrial base stations (BSs) face significant challenges in providing services to an increasing number of IoT devices and networks. Fortunately, unmanned aerial vehicles (UAVs) have been widely discussed for their ability to quickly deploy as air relays to boost the capacity and coverage of existing terrestrial networks and enhance the quality of wireless communication [3][4][5]. Due to the heterogeneity and geographical dispersion of IoT devices, several security issues have arisen.…”

Section: Introductionmentioning

confidence: 99%

A Robust Scheme for RIS-Assisted UAV Secure Communication in IoT

Qian

Zhang

Yan³

et al. 2023

Electronics

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Reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicles (UAV) have been extensively studied on the Internet of Things (IoT) systems to improve communication performance. In this paper, we aimed to counter simultaneous jamming and eavesdropping attacks by jointly designing an active beamforming vector at the base station (BS) and reflect phase shifts at the RIS. Specifically, considering imperfect angular channel state information (CSI), the sum secrecy rate maximization problem in the worst case could be formulated, which is NP-hard and non-convex. To address this problem, we improved the robust enhanced signal-to-leakage-and-noise ratio (E-SLNR) beamforming to reduce the computational complexity and mitigate the impact of interference, eavesdropping and jamming. Furthermore, a genetic algorithm with a tabu search (GA-TS) method was proposed to efficiently obtain an approximate optimal solution. The simulation results demonstrated that the proposed GA-TS method converged faster with better results than conventional GA, while the proposed robust scheme could achieve higher sum secrecy rates than the zero-forcing (ZF) and SLNR schemes.

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Integrated Spectrum Management Architecture Based on Spatial Spectrum Map in Multi-UAVs Networks

Zhang,

Qian,

et al. 2024

Lecture Notes in Electrical Engineering

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Joint mission planning and spectrum resources optimization for multi‐UAV reconnaissance

Cited by 5 publications

References 30 publications

Efficient three‐dimensional deployment of multiple unmanned aerial vehicles supporting ground base station toward maximizing served users with heterogeneous quality‐of‐service requirements

Efficient three‐dimensional deployment of multiple unmanned aerial vehicles supporting ground base station toward maximizing served users with heterogeneous quality‐of‐service requirements

A Robust Scheme for RIS-Assisted UAV Secure Communication in IoT

Integrated Spectrum Management Architecture Based on Spatial Spectrum Map in Multi-UAVs Networks

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