3D location optimization of multiple Unmanned Aerial Vehicle Base Stations in next-generation cellular networks and a new meta-heuristic approach Gelecek nesil hücresel ağlarda çoklu İnsansız Hava Aracı Baz İstasyonlarının 3D konum optimizasyonu ve yeni bir meta-sezgisel yaklaşım
Journal of the Faculty of Engineering and Architecture of Gazi University, cilt.36, sa.3, ss.1467-1482, 2021 (SCI-Expanded, Scopus, TRDizin)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 36 Sayı: 3
- Basım Tarihi: 2021
- Doi Numarası: 10.17341/gazimmfd.716111
- Dergi Adı: Journal of the Faculty of Engineering and Architecture of Gazi University
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Art Source, Compendex, TR DİZİN (ULAKBİM)
- Sayfa Sayıları: ss.1467-1482
- Anahtar Kelimeler: Aerial base station, Cellular networks, Electromagnetism-like algorithm, Multiple Drone-BS deployment, Whale optimization algorithm
- Van Yüzüncü Yıl Üniversitesi Adresli: Evet
Özet
The use of Unmanned Aerial Vehicles (UAVs) as mobile Base Stations (BSs) in wireless communication is emerging as an effective technique that should be used for the services planned in the next-generation cellular networks or in disaster situations. Currently, effective 3D placement of BS mounted on UAVs or Drones (Drone-BS), also called Low-altitude Air Platforms, in the defined area significantly increases the Quality of Service (QoS) of wireless communication. This study has been aimed to be performed dynamic deployments (location optimization) of Drone-BSs randomly distributed in the 3D plane in order to optimally cover the users in the urban environment according to the Air-to-Ground (ATG) model. Using new approaches based on the Electromagnetism-Like (EML) Algorithm and the Whale Optimization Algorithm (WOA), which are widely used in the literature and are meta-heuristic, it was planned to be covered the maximum number of users on the ground by multiple Drone-BSs. In addition, Extensive-interval Optimal Fitness Search Algorithm (EOFSA-EML) and Discrete-interval Optimal Fitness Search Algorithm (DOFSA-EML) approaches were developed based on the EML algorithm. Based on comparison metrics for multiple Drone-BSs distributed by EOFSA-EML, DOFSA-EML, and Pure-WOA, it has been found that EML-based algorithms achieve optimal results compared to Pure-WOA.