Intelligent UAV Deployment for a Disaster-Resilient Wireless Network

Abstract

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations (ABSs) has been considered to be a feasible solution to provide network coverage in scenarios where the conventional terrestrial network is overloaded or inaccessible due to an emergency situation. This article studies the problem of optimal placement of the UAVs as ABSs to enable network connectivity for the users in such a scenario. The main contributions of this work include a less complex approach to optimally position the UAVs and to assign user equipment (UE) to each ABS, such that the total spectral efficiency (TSE) of the network is maximized, while maintaining a minimum QoS requirement for the UEs. The main advantage of the proposed approach is that it only requires the knowledge of UE and ABS locations and statistical channel state information. The optimal 2-dimensional (2D) positions of the ABSs and the UE assignments are found using K-means clustering and a stable marriage approach, considering the characteristics of the air-to-ground propagation channels, the impact of co-channel interference from other ABSs, and the energy constraints of the ABSs. Two approaches are proposed to find the optimal altitudes of the ABSs, using search space constrained exhaustive search and particle swarm optimization (PSO). The numerical results show that the PSO-based approach results in higher TSE compared to the exhaustive search-based approach in dense networks, consuming similar amount of energy for ABS movements. Both approaches lead up to approximately 8-fold energy savings compared to ABS placement using naive exhaustive search.

Modeling Optimal Location Distribution for Deployment of Flying Base Stations as On-Demand Connectivity Enablers in Real-World Scenarios

“…The papers form four groups according to their goals. In the first group, the authors aim to minimize the number of required FBSs [ 22 , 23 , 24 , 25 , 26 ], in the second, to achieve maximum coverage of UEs [ 26 , 27 , 28 , 29 , 30 ], in the third, to maximize the network throughput [ 31 , 32 ], and, in the fourth, to maximize the spectral efficiency [ 33 ]. The optimization problem is either solved by existing algorithms or their combination [ 24 , 27 , 28 , 32 , 33 ] or a new algorithm is developed or derived from a previous algorithm [ 22 , 23 , 25 , 29 , 31 ].…”

Green Tethered UAVs for EMF-Aware Cellular Networks

“…In [22], the authors proposed an efficient cell-based allocation method, which provides an optimized unmanned aerial vehicle (UAV) positioning to enhance the performance of communication systems. Moreover, UAVs can hover over geographical regions that experience heavy traffic conditions due to natural disasters or mass events to assist the existing infrastructure in providing users with better service quality [23]. But the limited onboard energy and flight time impose a critical challenge for the deployment of UAVs [24].…”

A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks

“…Once the devices are clustered, the UAV position and trajectory plays a crucial role in determining the Energy Efficiency (EE) of the network. Mainly, in the disaster situation the UAV acts as a relay node for on-scene devices [24][25][26][27][28][29][30][31][32][33]. For UAV deployment in PSN, the authors in [27] discovered the optimal altitude for a UAV that maximizes coverage.…”

“…In [31], authors used Reinforcement Learning (RL) technique to deploy UAVs in a disaster scenario to maximize total user coverage. In [32], the intelligent placement of UAVs as temporary aerial base stations is discussed for public safety communications. In [34], authors proposed a UAV-assisted vehicular communication framework using Software Defined Networking (SDN) to reduce the processing cost of vehicles.…”