Abstract
Having reliable telecommunication systems in the immediate aftermath of a catastrophic event makes a huge difference in the combined effort by local authorities, local fire and police departments, and rescue teams to save lives. This paper proposes a physical model that links base stations that are still operational with aerial platforms and then uses a machine learning framework to evolve ground-to-air propagation model for such an ad hoc network. Such a physical model is quick and easy to deploy and the underlying air-to-ground (ATG) propagation models are both resilient and scalable and may use a wide range of link budget, grade of service (GoS), and quality of service (QoS) parameters to optimise their performance and in turn the effectiveness of the physical model. The prediction results of a simulated deployment of such a physical model and the evolved propagation model in an ad hoc network offers much promise in restoring communication links during emergency relief operations.
Highlights
During man-made or natural disaster situations, wireless communication systems have the additional merit that they are less vulnerable to physical damage
Their results show that Long-Term Evolution (LTE) outperforms WiFi under all conditions, while it is inferred that cost, coverage, and deployment time should be considered for suitable selection of technology for low aerial platforms
Their results confirm the advantage of deploying aerial platforms in terms of high bandwidth utilization, wide coverage, and required number of base stations to cover a specified area in relation portable terrestrial stations (PTSs)
Summary
During man-made or natural disaster situations, wireless communication systems have the additional merit that they are less vulnerable to physical damage. Disruptions caused by physical damage can be incredibly costly and time consuming to restore, as they require maintenance or sometimes replacement of complex network hardware to re-establish communications This is especially problematic if major installations such as terrestrial towers or fibre-optic cables are involved [4,5,6,7]. This paper proposes a physical model that uses aerial platforms for re-establishing connectivity in the immediate aftermath of a natural disaster and uses a machine learning framework to evolve an ATG propagation model at different altitudes. The evolved propagation model considers optimal altitudes and elevation angles of the constituent aerial platforms to achieve connectivity with assured QoS and GoS for rescue teams in a typical urban and dense population environment Such optimization would help in meeting both the demand and nature of an unplanned event.
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