Optimization of aerial base station location in LAP for disaster situations

Abstract

Aerial telecommunication platforms will be a part of the future communication networks. In this paper, we address the issue of optimizing the location of Aerial Base Stations (ABS) in Low Altitude Platforms (LAP) to minimize the power consumption. We propose a dynamic algorithm which attempts to maximize the received power at ABS by moving it between multiple points within its coverage area. Furthermore, outage probability formula for each user has been derived to measure how much gain this algorithm can offer in line of sight (LOS) and non line of sight (NLOS) cases. Simulation results show that the employment of this algorithm saves more than 10 dB per user in case of Poisson distributed users.

Spatial Configuration of Agile Wireless Networks With Drone-BSs and User-in-the-loop

“…Environmental parameters depend on the average characteristics of urban areas, such as the density and average 3 The model is used in the following publications and many others: [? ], [8], [14], [18], [81], [82]. height of the buildings [80].…”

Deployment of an aerial platform system for rapid restoration of communications links after a disaster: a machine learning approach

“…In [13] an optimal aerial platform location is examined for emergency conditions to minimize PL and maximize RSS. An ATG propagation model is considered at an altitude of 1 km, 2 GHz frequency band and transmission power set at 40 dBm.…”

“…This subsection discusses issues that may affect the performance of aerial platforms such as frequency band, antenna specifications, interference, delay, and power consumption. In aerial platform technology, both WiMAX and LTE perform well and are suggested as good candidates for better coverage, whether in LoS or nLoS, increased capacity and less interference [13,[28][29][30]. The researchers in [31] present two scenarios of disaster monitoring and disaster relief using aerial platforms.…”

Robust optimal control of a nonlinear surface vessel model with parametric uncertainties

“…For instance, the commonly used ship motion model provided by the Maneuvering Modelling Group (MMG) has 17 coefficients for the hull's hydrodynamic coefficient alone, making the optimization problem complex and large [13]. To address this issue, optimization techniques such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) [14][15][16] have been used to determine the optimal coefficients that best match the ship's motion. However, these methods can be slow, particularly if a large number of samples [17] are needed to represent the uncertainties in the hydrodynamic coefficients.…”