The base station installed on the unmanned aerial vehicle, referred to as the UAV-mounted base station (UBS), is considered an innovative paradigm for restoring cellular connectivity in areas where a
In this paper, we present a mathematical model for FBS deployment in large-scale scenarios. The model is based on a location set covering problem and the goal is to minimize the number of
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This paper solely focuses on the deployment of base stations in two-dimensional scenarios, employing an idealized model that overlooks the attenuation and interference of specific signal
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We extract the horizontal deployment problem into the solution of UAV coverage rate and connectivity rate and calculate the optimal horizontal deployment coordinates of the UAV base stations, which effectively
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Firstly, the theoretical minimum value of HDOP is calculated for different numbers of base stations. Then, the relationship among the number of base stations, deployment methods, and HDOP is studied. Finally, an
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In this paper, we present a mathematical model for FBS deployment in large-scale scenarios. The model is based on a location set covering problem and the goal is to minimize the number of
Get Price
The implementation details of the Deploy component are elaborated upon in Section 6. For the centralized or distributed-centralized strategies, the deployment strategy is initially computed at the remote station (or secondary stations) and subsequently communicated to the UAV-BSs.
By equipping UAVs with communication units to function as aerial base stations, wireless connections can be established with ground users to improve the quality of service (QoS for short), thereby compensating for the limitations of terrestrial communication systems in terms of flexibility and coverage range.
Firstly, the count of UAV-BSs and user distributions fluctuate dynamically over time. This dynamism complicates the task of crafting a consistent UAV-BS deployment strategy that optimizes QoS. Furthermore, any change in user distributions or the number of UAV-BSs necessitates an adjustment to the deployment strategy.
However, deploying UAV-BSs faces challenges including the cooperation of multiple UAVs, dynamic user distribution, low-reliability issues of UAVs, and efficient redeployment in large environments. Existing literature addresses some of these challenges but lacks a comprehensive approach.
According to the Partition component, the deployment of UAV-BSs in time slot t can be represented by S t = ⋃ A ′ ∈ L e a f (T (A)) S t (A ′). Therefore, instead of constructing S t globally, the Deploy component generates S t (A ′) for each leaf node A ′ ∈ L e a f (T (A)) locally.
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