On the Influence of Charging Stations Spatial Distribution on Aerial Wireless Networks

Abstract

Using drones for cellular coverage enhancement is a recent technology that has shown a great potential in various practical scenarios. However, one of the main challenges that limits the performance of drone-enabled wireless networks is the limited flight time. In particular, due to the limited on-board battery size, the drone needs to frequently interrupt its operation and fly back to a charging station to recharge/replace its battery. In addition, the charging station might be responsible to recharge multiple drones. Given that the charging station has limited capacity, it can only serve a finite number of drones simultaneously. Hence, in order to accurately capture the influence of the battery limitation on the performance, it is required to analyze the dynamics of the time spent by the drones at the charging stations. In this paper, we use tools from queuing theory and stochastic geometry to study the influence of each of the charging stations limited capacity and spatial density on the performance of a drone-enabled wireless network.

Multiagent Deep Reinforcement Learning for Wireless-Powered UAV Networks

“…In another study [26], the authors focused on minimizing the number of CSs and optimizing their deployment where UAVs can recharge their batteries and then fly again. Also, the influence of CSs on network performance is studied in [27]. This study concluded that achieving an optimized coverage can be reached by reducing the charging time and deploying lower dense CSs.…”

“…When an action a n t is executed, FES n receives a reward r n t and transits to the next state s t+1 . Then, an observation is made to see if FES n have exceeded their restrictions (see (27), (28), and ( 29)). If this is the case, a set of penalties is calculated in Line 17.…”

Energy Efficiency Analysis of Charging Pads-Powered UAV-Enabled Wireless Networks

“…For this setup, the authors first derived accurate expressions of coverage probability and average data rate and then obtained the approximated equations by using the upper bound of the Gamma distribution. Author in [27]- [29] studied the availability probability and revised coverage probability of UAVs subject to the limited capacity of charging stations, where the locations of UAVs and charging stations were modeled as two independent PPPs, and users were modeled as MCP. In [8], [30], [31], authors studied a system with tethered UAV, which denotes a UAV connected to a ground station to get a stable power supply and a reliable data link but with restricted mobility.…”

“…Here, switching frequency denotes the number of changing of density pairs between (λ m,h ,λ n,l ) and (λ m,l ,λ n,h ) per day. Different from the previous work [27], we use charging pads in this work to prolong the service time of UAVs, e.g., no need to charge the battery and travel to charging stations, hence, provide the service all the time. By doing so, UAVs can provide service without being interrupted.…”

“…Note that the subscripts {s 1 , s 2 } denote the first and the second scenario, where the densities of UAVs are different, λ u,1 and λ u,2 , respectively, Proof: The Laplace transform is computed using MGF of Gamma distribution, PGFL of inhomogeneous PPP, similar to [27] Lemma 7.…”