Multi-tier Drone Architecture for 5G/B5G Cellular Networks: Challenges, Trends, and Prospects
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Abstract (arXiv)
Drones (or unmanned aerial vehicles [UAVs]) are expected to be an important component of fifth generation (5G)/beyond 5G (B5G) cellular architectures that can potentially facilitate wireless broadcast or point-to-multipoint transmissions. The distinct features of various drones such as the maximum operational altitude, communication, coverage, computation, and endurance impel the use of a multi-tier architecture for future drone-cell networks. In this context, this article focuses on investigating the feasibility of multi-tier drone network architecture over traditional single-tier drone networks and identifying the scenarios in which drone networks can potentially complement the traditional RF-based terrestrial networks. We first identify the challenges associated with multi-tier drone networks as well as drone-assisted cellular networks. We then review the existing state-of-the-art innovations in drone networks and drone-assisted cellular networks. We then investigate the performance of a multi-tier drone network in terms of spectral efficiency of downlink transmission while illustrating the optimal intensity and altitude of drones in different tiers numerically. Our results demonstrate the specific network load conditions (i.e., ratio of user intensity and base station intensity) where deployment of drones can be beneficial (in terms of spectral efficiency of downlink transmission) for conventional terrestrial cellular networks.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Nov 2017.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 2.
4 papers.
- 2024Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Modelscited by 3
- 2023Reliable and Efficient Data Collection in UAV-based IoT Networkscited by 2
- 2025Base Station Deployment under EMF constrain by Deep Reinforcement learningcited by 1
- 2025Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approachcited by 0
- 2025Base Station Deployment under EMF constrain by Deep Reinforcement learning23 Dec 2025
- 2025Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approach9 Aug 2025
- 2024Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models24 Jan 2024
- 2023Reliable and Efficient Data Collection in UAV-based IoT Networks9 Nov 2023