Optimal 3D UAV base station placement by considering autonomous coverage hole detection, wireless backhaul and user demand

Al-Ahmed, Shahriar Abdullah; Shakir, Muhammad Zeeshan; Zaidi, Syed Ali Raza

doi:10.23919/jcn.2020.000034

Cited by 59 publications

(15 citation statements)

References 25 publications

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“…The approach uses the strategy of parameter mutation combined with virtual force to achieve flexibility in the instantaneous motions of UAVs to achieve significant improvements in key performance indicators. The work of [25] proposes an approach to minimize the time to detect power outages in the main grid and provide on-demand coverage based on UAV-BSs. The proposal uses UAV-BSs and a Q-learning algorithm to autonomously detect coverage gaps and then deploy wireless communication devices considering wireless backhaul and user demand.…”

Section: Related Workmentioning

confidence: 99%

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Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

Alfaia

Souto

Cardoso

et al. 2022

Drones

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The rapid growth of data traffic due to the demands of new services and applications poses new challenges to the wireless network. Unmanned aerial vehicles (UAVs) can be a solution to support wireless networks during congestion, especially in scenarios where the region has high traffic peaks due to the temporal and spatial flow of users. In this paper, an intelligent machine-learning-based system is proposed to deploy UAV base stations (UAV-BS) to temporarily support the mobile network in regions suffering from the congestion effect caused by the high density of users. The system includes two main steps, the load prediction algorithm (LPA) and the UAV-BSs clustering and positioning algorithm (UCPA). In LPA, the load history generated by the mobile network is used to predict which macrocells are congested. In UCPA, planning is performed to calculate the number of UAV BSs needed based on two strategies: naïve and optimized, in addition to calculating the optimal positioning for each device requested to support the overloaded macrocells. For prediction, we used two models, generalized regression neural networks (GRNN) and random forest, and the results showed that both models were able to make accurate predictions, and the random forest model was better with an accuracy of over 85%. The results showed that the intelligent system significantly reduced the overhead of the affected macrocells, improved the quality of service (QoS), and reduced the probability of blocking users, as well as defined the preventive scheduling for the UAV BSs, which benefited the scheduling and energy efficiency.

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Section: Related Workmentioning

confidence: 99%

“…Similarly, in [43,45], the required throughput of a backhaul is calculated based on the cumulative radio throughput of the users served by the macrocell j ∈ M. The required throughput is calculated in Equation (25).…”

Section: Set Of Cluster Centroid P Uavmentioning

confidence: 99%

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

Alfaia

Souto

Cardoso

et al. 2022

Drones

View full text Add to dashboard Cite

show abstract

“…Existing works in the literature on UAV placement use static optimization algorithms for selecting optimal UAV locations based on various goals and objectives, e.g., maximal coverage [7], minimizing coverage holes [8], [9]. Most works assume the availability of back-haul networks or use ground communication infrastructure as a supplement to UAV networks.…”

Section: Related Workmentioning

confidence: 99%

Resilient UAV Formation for Coverage and Connectivity of Spatially Dispersed Users

Wang¹,

Farooq²

2022

Preprint

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Unmanned aerial vehicles (UAVs) are a convenient choice for carrying mobile base stations to rapidly setup communication services for ground users. Unlike terrestrial networks, UAVs do not have fiber optic backhaul connectivity except when they are tethered to the ground, which restricts their mobility. In the absence of back-haul, e.g., in remote areas, emergency situations, or in battlefields, there is a need to ensure connectivity among UAVs in addition to coverage of ground users for creating local area networks. This paper provides a distributed and dynamic approach for UAV formationbased control for coverage and connectivity of spatially dispersed users. We use flocking dynamics as a guide to constructing tailored formations of UAVs on the fly. Simulation results demonstrate that if sufficient aerial base stations are available, the proposed approach results in a strongly connected network of UAVs that is able to provide both a backhaul and fronthaul network. The approach can be further extended to create multi-tier extra-terrestrial networks to cater for large-scale applications.

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“…• Reinforcement Learning (RL): This ML is about learning the optimal action or behaviour in an unknown environment based on a trial and error mechanism [30,31]. The RL agent takes actions in the given environment where it receives positive and negative rewards [25].…”

Section: For Labelling the Data And Pre-dictionmentioning

confidence: 99%

Semi-supervised learning-based coverage hole detection in cellular networks

Al-Ahmed¹,

Shakir²

2022

ITU J-FET

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For any time-critical mobile network-dependent applications and services, coverage is one of the prominent factors for providing the best Quality of Service (QoS) and Quality of Experience (QoE). A simple Coverage Hole (CH) may degrade the performance and the reputation of any operator by reducing the Key Performance Indicators (KPIs). This is one of the important aspects which need to be planned from the phase of network deployment throughout the whole operational stage. Many factors can cause CH such as attenuation, obstacles and improper network planning. Traditionally, a Drive Test (DT) used to be carried out in order to assess the quality of the mobile network signal. With technological advancement, DT has been replaced by the Minimization of Drive Test (MDT) and included as a part of Self-Organizing Networkss (SONs). The MDT process is applicable to networks that operate in 3G, 4G and 5G technologies. With this method, operators are able to measure network performance with the help of end users' devices. Thus, the network can be managed more conveniently, performance is improved, quality is increased, and maintenance costs are reduced for the network. However, the processing of MDT at the operators' side remains time-consuming and complex especially for CH analysis and detection from mobile network data. Therefore, we present a method by utilising Semi-Supervised Learning (SSL) in this paper so that this task becomes uncomplicated with improved accuracy. Our results show that the proposed method achieves better accuracy than the usual classification algorithm.

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Optimal 3D UAV base station placement by considering autonomous coverage hole detection, wireless backhaul and user demand

Cited by 59 publications

References 25 publications

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

Resource Management in 5G Networks Assisted by UAV Base Stations: Machine Learning for Overloaded Macrocell Prediction Based on Users’ Temporal and Spatial Flow

Resilient UAV Formation for Coverage and Connectivity of Spatially Dispersed Users

Semi-supervised learning-based coverage hole detection in cellular networks

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