On Energy Consumption of Airship-Based Flying Base Stations Serving Mobile Users

Bečvář, Zdeněk; Nikooroo, Mohammadsaleh; Mach, Pavel

doi:10.1109/tcomm.2022.3196654

Cited by 11 publications

(1 citation statement)

References 48 publications

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“…In Zhao et al and Wang et al, 20,21 a centralized greedy search algorithm and distributed motion control algorithm were adopted to optimally deploy multiple UAVs for on-demand coverage of ground user equipment, where UAVs formed a bi-connected network to maximize load balancing among them. Becvar et al 22 derived an analytical relation between the sum capacity of the users and the energy spent of airship-based flying base stations and optimized theoretical bounds of potential energy saving with respect to a relative sum capacity guarantee to the users. The work of Olmedo et al 23 examined the implementation of an aerial emergency communications network based on a prototype airship as a valid options to provide rapidly deployable and resilient communications for disaster relief.…”

Section: State Of the Artmentioning

confidence: 99%

Optimal deployment strategy of airships for near‐space communication system using multiobjective advanced evolutionary algorithm

Alam,

Mondal

2023

Int J Communication

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SummaryThis paper presents a near‐space communication system (NSCS) using advanced deployment strategies to gain high throughput. The airships are deployed according to the user's location, assuming robust backbone network characteristics such as signal path loss, fading factor, routing efficiency, and safety issues among bi‐connected airships. Due to the independent flying nature of airships, it is very attractive to deploy them as aerial base stations and construct airborne networks to provide service for on‐ground users. However, it is quite challenging to optimally deploy multiple airships for on‐demand coverage while maintaining the connectivity among airships. A balance between the network parameters, i.e., capacity and coverage area, should be maintained for optimal deployment of the airships. We have derived the maximum throughput of NSCS, including system parameters, as a multiobjective optimization problem subjected to efficient routing protocol and safety constraints. A decomposition‐based advanced multiobjective evolutionary algorithm (AMOEA/D) is adopted to solve the deployment optimization problem. The proposed algorithm is motivated by the non‐dominated solutions that maintain population diversity over the variable space. Two designed test problems, that is, the L‐shaped hotspot problem and nine hotspot problems, are also investigated. Numerical results show that the proposed method improves the system performance compared with benchmark external archive‐guided MOEA/D (EGA‐MOEA/D) and non‐dominated sorted genetic algorithms (NSGA‐II) by 10.46% and 3.84%, respectively.

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