Mobility Prediction For Aerial Base Stations for a Coverage Extension in 5G Networks

Chaalal, Elhadja; Reynaud, Laurent; Senouci, Sidi‐Mohammed

doi:10.1109/iwcmc51323.2021.9498892

Cited by 8 publications

(10 citation statements)

References 15 publications

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“…Simulation results showed that the GA algorithm outperforms SA regarding fitness value and execution time. In [28], A Social Spider Optimization (SSO) Algorithm was employed to solve the problem of UAV deployment. An evaluation of its effectiveness was conducted in three different areas serving a different number of users, and a comparison was made with the Random Search (RS) method and the Uniform Distribution application.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

et al. 2023

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The concept of smart cities is to enhance the life quality of residents and provide efficient services by integrating advanced information and communication technologies, autonomous robots, Internet of Things (IoT) devices, etc. Unmanned Aerial Vehicles (UAVs) are a class of autonomous flying mobile robots that bring a lot of benefits to smart cities due to their mobility, accessibility, autonomy, and many other advantages. Their integration allows for accomplishing hard and complex tasks that humans or other entities are not able to complete. In most applications, multiple connected UAVs are required to build a network under which missions are completed and tasks are shared. This network can be established by finding the optimal UAV placement that meets some requirements such as user coverage, UAV connectivity, energy, and load distribution. In this paper, we present a hybrid algorithm, called IMRFO-TS, based on the hybridization of Improved Manta Ray Foraging Optimization (IMRFO) with the Tabu Search (TS) algorithm for solving the UAV placement problem in a smart city. First, the tangential control strategy is incorporated into the original MRFO algorithm to enhance its convergence speed and explore the search space effectively. Second, The TS algorithm is hybridized with the IMRFO algorithm to increase the exploitation capability of IMRFO and improve the best solution (placement quality) obtained after each iteration. The performance of the proposed IMRFO-TS algorithm is validated using 52 benchmarks considering the fitness value, coverage, connectivity, energy consumption, and load distribution parameters. Compared to eight well-known optimization meta-heuristics such as the original MRFO, TS algorithm, Bat Algorithm (BA), Firefly Algorithm (FA), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Reptile Search Algorithm (RSA), the results of the experiments revealed the significant superiority of the proposed IMRFO-TS algorithm by obtaining promising solutions (optimal positions of UAVs) in the majority of the cases.

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Section: Literature Reviewmentioning

confidence: 99%

“…At this level, the population is initialized and the IMRFO-TS starts evaluating the individuals. For each individual, the fitness value is calculated using Equation (28).…”

Section: B Evaluationmentioning

confidence: 99%

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

et al. 2023

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“…Furthermore, because of their controlled mobility and customizable heights, UAVs are the best choice for improving performance and overcoming the limitations of terrestrial networks. The industries and academics are developing a wireless infrastructure UAV (WI UAV) that can offer wireless service on the move and dynamically re-position itself to increase spectrum efficiency, availability, coverage, and enduser quality of experience (QoE) [7]. For this, WI UAV can serve as a flying relay node or a base station (BS) or a data collector/disseminator [8].…”

Section: Introductionmentioning

confidence: 99%

“…WI UAV can extend the accessibility of a cellular network or boost its capacity in areas where conventional networks are difficult to deploy [7]. It facilitates fast deployment in disasters and crises, such as aiding with evacuation if communications infrastructure is disrupted or delivering capacity for overcrowded events on demand [1], [2].…”

Section: Introductionmentioning

confidence: 99%

6G NR-U Based Wireless Infrastructure UAV: Standardization, Opportunities, Challenges and Future Scopes

et al. 2022

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Providing ubiquitous connectivity for people and equipment with a wide range of service conditions is regarded as the major design goal of 6G networks. Moreover, the future 6G networks need to be extremely flexible to enable reliable and low-latency access for the dynamic number of mobile user equipment. The current base stations (BSs) and remote relay antennas are mostly, on the other hand, installed at fixed geographic locations based on long-term traffic patterns with little re-deployment flexibility. For most 6G applications, where dynamic data traffic occurs in both spatial and temporal domains, such rigid radio access networks (RANs) are unable to maintain ubiquitous connectivity. On the other hand, the rapid advancement of unmanned aerial vehicle (UAV) technology is creating a unique opportunity for the cellular operator to realize flying wireless infrastructure called wireless infrastructure UAV (WI UAV). WI UAV supports wireless connectivity and can stir itself to improve spectral efficiency, coverage, and quality of service of the end-users. This paper introduces 6G new radio in the unlicensed band (NR-U)-based WI UAV that can be used as a base station, relay, or data collector/disseminator. UAV are categorized based on their characteristics, applications, and operations. Further, this paper discusses various regulatory and standardization initiatives for integrating UAVs into the cellular network. Non-standalone NR-U network architecture is designed and explained for WI UAV. Several opportunities and design challenges of NR-U for the WI UAV are discussed and future scopes of WI UAVs are presented.

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“…Others [163] Self-attention-based transformer models are utilized to predict users' spatial distribution, guiding UAV Base Stations to adjust their positions for optimal network coverage.…”

Section: Diffusion [156]mentioning

confidence: 99%

A Study Of Civil Engineering Education At Singapore Nanyang Technology University And At The University Of Florida

Liu¹,

Najafi²

2002 Annual Conference Proceedings

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offer civil engineering courses to undergraduate and graduate students. This paper discusses and compares Civil Engineering curriculum in both Universities. The Civil Engineering courses at the University of Florida are divided into two phases of general education and upper division/civil engineering core education. At Nanyang Technology University, the Civil Engineering offers courses of a prescribed core of essential subjects and both institutions offer excellent program of study. The Civil Engineering program at Nanyang Technology University offers courses closely related to industry and government needs. Similarly, at the University of Florida, Civil Engineering program also offers excellent education program in line with government and industries needs. At both institutions, efforts should be focused on revising curriculum that is more responsible to existing and future rapid technological changes.

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Mobility Prediction For Aerial Base Stations for a Coverage Extension in 5G Networks

Cited by 8 publications

References 15 publications

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

6G NR-U Based Wireless Infrastructure UAV: Standardization, Opportunities, Challenges and Future Scopes

A Study Of Civil Engineering Education At Singapore Nanyang Technology University And At The University Of Florida

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