The New Frontier in RAN Heterogeneity: Multi-tier Drone-Cells
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Abstract (arXiv)
In cellular networks, the locations of the radio access network (RAN) elements are determined mainly based on the long-term traffic behaviour. However, when the random and hard-to-predict spatio-temporal distribution of the traffic (load,demand) does not fully match the fixed locations of the RAN elements (supply), some performance degradation becomes inevitable. The concept of multi-tier cells (heterogeneous networks, HetNets) has been introduced in 4G networks to alleviate this mismatch. However, as the traffic distribution deviates more and more from the long-term average, even the HetNet architecture will have difficulty in coping up with the erratic supply-demand mismatch, unless the RAN is grossly over-engineered (which is a financially non-viable solution). In this article, we study the opportunistic utilization of low-altitude unmanned aerial platforms equipped with base stations (BSs), i.e., drone-BSs, in 5G networks. In particular, we envisage a multi-tier drone-cell network complementing the terrestrial HetNets. The variety of equipment, and non-rigid placement options allow utilizing multitier drone-cell networks to serve diversified demands. Hence, drone-cells bring the supply to where the demand is, which sets new frontiers for the heterogeneity in 5G networks. We investigate the advancements promised by drone-cells, and discuss the challenges associated with their operation and management. We propose a drone-cell management framework (DMF) benefiting from the synergy among software defined networking (SDN), network functions virtualization (NFV), and cloud-computing. We demonstrate DMF mechanisms via a case study, and numerically show that it can reduce the cost of utilizing drone-cells in multitenancy cellular networks.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Apr 2016.
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Citing papers per year: 2022: 2; 2023: 1.
3 papers.
- 2022Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Controlcited by 5
- 2023Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systemscited by 5
- 2022UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operationscited by 1
- 2023Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems9 Feb 2023
- 2022Cooperative Multi-Agent Deep Reinforcement Learning for Reliable and Energy-Efficient Mobile Access via Multi-UAV Control3 Oct 2022
- 2022UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations17 Feb 2022