3-D Placement of UAVs Based on SIR-Measured PSO Algorithm

Liu, Wentao; Niu, Guanchong; Cao, Qi; Pun, Man-On; Chen, Junting

doi:10.1109/gcwkshps45667.2019.9024696

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…Other researchers identify concrete wireless technology, such as cellular technologies like LTE and 3GPP, as evidenced in papers [164], [175], [177]. Wi-Fi at 2.4 GHZ is also employed in some papers such as [127], [128], [134], [161], [171]. We also found that Free Space Optics technology is used [106].…”

Section: ) Simulation Settingsmentioning

confidence: 62%

“…This approach enhances the utilization of available resources and promotes a more financially viable network deployment. In this SMS, 12.3% of the contributions (i.e., [78], [95], [103], [117], [120], [155], [156], [158], [161], [164], [166], [168], [171], [172], [178], [180]) focus on optimizing the number of UAVs within the network. For example, Sawalmeh et al [120] address multiple objectives, aiming to maximize wireless coverage while minimizing the number of UAVs and their power transmission.…”

Section: • Number Of Uavsmentioning

confidence: 99%

“…Particle Swarm Optimization (PSO) or its variants are favored by 14.6% of researchers due to their capacity to reduce complexity, solve problems in polynomial time, and provide minimal errors. Notable articles like [116], [123], [124], [126], [149], [158], [161] leverage this technique. For example, Chen et al [170] utilize Multi-PSO, an optimized PSO algorithm.…”

Section: ) Solution Strategymentioning

confidence: 99%

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3D Placement Optimization in UAV-Enabled Communications: A Systematic Mapping Study

Carvajal-Rodríguez,

Guamán,

Tipantuña

et al. 2024

IEEE Open J. Veh. Technol.

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Unmanned aerial vehicles (UAVs) have seen remarkable improvements in their flight capabilities and computational power. Their applications span various domains, such as agriculture, military, and communications. To improve communication quality and coverage in UAV-enabled systems, understanding effective placement techniques is crucial. While research has explored optimal UAV placement in two dimensions, there is a need for a comprehensive three-dimensional (3D) analysis, particularly for resource optimization. This article presents a systematic mapping study on 3D placement in UAV-enabled communication systems, incorporating a threat analysis for the reliability of the results. It provides valuable insights for future research on 3D placement optimization techniques, specifically examining optimization objectives, model system features, problem types, and solution strategies. Our research reveals, on one hand, an emphasis on optimizing data rate throughput, power transmission, and ground coverage, and a predominant use of large-scale fading in the air-to-ground channel model; on the other hand, heuristic algorithms prevail as the solution strategy. Moreover, we identify gaps in achieving critical objectives such as the probability of outage, deployment costs, quality of experience, and spectrum optimization. This highlights the need for further exploration in the formulation aspects of the system, which includes small-scale fading, access techniques, and interference management.

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Section: ) Simulation Settingsmentioning

confidence: 62%

Section: • Number Of Uavsmentioning

confidence: 99%

Section: ) Solution Strategymentioning

confidence: 99%

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3D Placement Optimization in UAV-Enabled Communications: A Systematic Mapping Study

Carvajal-Rodríguez,

Guamán,

Tipantuña

et al. 2024

IEEE Open J. Veh. Technol.

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“…The use of multiple UAVs within an environment is also an emerging research field, seeking to expand the scope and scale of problems for which UAVs can offer a solution. 45.6% of the papers reviewed declare an interest in multi-UAV planning problems, most notably when considering path-planning in relation to the effective placement of multiple UAVs within a single physical environment based upon a given communication scenario [87,104]. Similarly, once the availability of multiple UAVs is envisioned, both [69,82] seek to minimise the number of UAVs required to serve the number of users encountered and optimise trajectories to effectively facilitate data delivery in vehicular networks where vehicle presence varies.…”

Section: Uav Related Attributesmentioning

confidence: 99%

Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

2023

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Unmanned aerial vehicles (UAVs) have the potential to make a significant impact in a range of scenarios where it is too risky or too costly to rely on human labour. Fleets of autonomous UAVs, which complete tasks collaboratively while managing their basic flight and related tasks independently, present further opportunities along with research and regulatory challenges. Improvements in UAV construction and components, along with developments in embedded computing hardware, communication mechanisms and sensors which may be mounted on-board a UAV are nearing the point where commercial deployment of fleets of autonomous UAVs will be technically possible. To fulfil this potential, UAVs will need to operate safely and reliably in complex and potentially dynamically changing environments with path-planning, obstacle sensing and collision avoidance paramount. This survey presents an original environment complexity classification, and critically analyses the current state of the art in relation to UAV path-planning approaches. Moreover, it highlights the existing challenges in environment complexity modelling and representation, path-planning approaches, and outlines open research questions together with future directions.

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“…In this paper, we focus on the problem of minimizing the number of deployed UAVs based on our previous work [21], while optimizing their deployment locations and transmission power, subject to a minimum SINR value for all users. This task is very challenging as it includes three highly coupled design goals, i.e.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization for Interference-Limited Unmanned Aerial Vehicle-Assisted Networks

Niu

Cao

et al. 2020

IEEE Access

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This work investigates the deployment of unmanned aerial vehicles (UAVs) as access points to provide wireless services to users in a green field. Three fundamental deployment designs are studied under practical air-to-ground (ATG) channel models, namely the minimum number of UAVs, their optimal deployment locations and the optimal transmit power allocation. Since these three design goals are coupled, a particle swarm optimization (PSO)-based scheme is proposed in conjunction with the balanced Signal to Interference plus Noise Ratio (SINR) transmit power allocation. By exploiting the closed-form expressions of the SINR-balanced optimal power allocation and the resulting SINR, the proposed PSO-based scheme iteratively optimizes the UAV number and subsequently, their locations until the resulting SINR of each user meets its required minimum value. To improve the convergence behavior of the proposed scheme, two schemes are devised to provide an initial estimate on the minimum number of UAVs by analyzing the system sum-rate capacity before using a K-means clustering technique to initialize the UAV locations. Finally, a power fine-tuning scheme is developed to further reduce the total transmit power. Extensive simulation is performed to confirm the good performance of the proposed schemes. INDEX TERMS Unmanned aerial vehicles, particle swarm optimization, radio resource management.

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3-D Placement of UAVs Based on SIR-Measured PSO Algorithm

Cited by 7 publications

References 8 publications

3D Placement Optimization in UAV-Enabled Communications: A Systematic Mapping Study

3D Placement Optimization in UAV-Enabled Communications: A Systematic Mapping Study

Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

Particle Swarm Optimization for Interference-Limited Unmanned Aerial Vehicle-Assisted Networks

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