On-Demand Ultra-Dense Cloud Drone Networks: Opportunities, Challenges and Benefits

Sharma, Navuday; Magarini, Maurizio; Jayakody, Dushantha Nalin K.; Sharma, Vishal; Li, Jun

doi:10.1109/mcom.2018.1701001

Cited by 51 publications

(23 citation statements)

References 14 publications

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“…Since the UAV‐enabled MEC is envisioned as a promising approach to enlarge the service scope of MEC, a considerable amount of research has been conducted on the UAV‐enabled MEC architecture [20–22]. In [20, 21], the researchers did a comprehensive survey of the future applications of UAV, both of which prospected the great potential of UAV in MEC.…”

Section: Related Workmentioning

confidence: 99%

UAV‐enabled computation migration for complex missions: A reinforcement learning approach

et al. 2020

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Section: Related Workmentioning

confidence: 99%

UAV‐enabled computation migration for complex missions: A reinforcement learning approach

et al. 2020

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“…Recently, an unmanned aerial vehicle (UAV) has been used in many applications, such as traffic monitoring [12], data transmission [13], communication enhancement [14,15], and so on. In order to solve the fixed energy source problem in the conventional WPT system, UAV can be used due to its inherent agility and on-demand quality [16], which has attracted considerable attention in the IoT [17,18,19,20].…”

Section: Related Workmentioning

confidence: 99%

“…WPT has been widely used in portable electronic devices, implanted medical devices, smart homes, electric vehicles (EVs), and so on [9]. Applying the WPT to wireless communication networks can permit wireless powered communication networks (WPCN) to be constructed and can effectively improve the energy efficiency of the network [10,11,12,13,14,15,16,17,18,19]. Compared with the conventional WPT system with a relatively fixed energy source, UAV can dynamically move the IoT devices around, power the IoT devices, and realize the data gathering and transmitting services [20].…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things

Liu

Zhou

2019

Sensors

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Most of the wireless nodes in the Internet of Things (IoT) environment face the limited energy problem and the way to provide a sustainable energy for these nodes has become an urgent challenge. In this paper, we present an unmanned aerial vehicle (UAV) to power the wireless nodes in the IoT and an investigation on the optimal resource allocation approach based on dynamic game theory. This IoT system consists of one UAV as the power source and information receiver. The wireless nodes can be powered and collected by the UAV. In order to distinguish the wireless nodes, the wireless nodes are divided into two categories based on the energy consumption. The UAV tries to power these two categories of nodes using a different power level based on the proposed approach, where the wireless nodes control the resources for information transmission. Based on Bellman dynamic programming, the optimal allocated resources for power transfer and information transmission are obtained for both the UAV and wireless nodes, respectively. In order to show the effectiveness of the proposed model and approach, we present numerical simulations.

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“…Essentially, ABSs are highly useful for servicing flash crowds such as festivals, carnivals, musical concerts, sport events etc., where large group of people demand for high data rates [4]. This also conducts to compact and small cell architectures, leading to the concept of ultra-dense cloud drone networks [5].…”

Section: Introductionmentioning

confidence: 99%

Impact of CFO on Low Latency-Enabled UAV Using "Better Than Nyquist" Pulse Shaping in GFDM

Sharma

Kumar

Magarini

et al. 2019

2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring)

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Low altitude aerial base stations onboard unmanned aerial vehicles (UAVs) have recently gained a lot of attention to provide cellular coverage to mobile ground users. In this paper we evaluate the effect of carrier frequency offset (CFO) on the symbol error rate in the downlink of such systems, where a long-term evolution (LTE) compatible time-frequency grid based on generalized frequency division multiplexing (GFDM) is considered. The choice of GFDM as multi-carrier modulation scheme is motivated by its widely proposed application in fifth generation cellular systems and its backward compatibility with LTE. Simulation results are shown for "Better than the Nyquist (BTN)" pulse shaping filters in three different environments: Suburban, Urban and Urban High Rise. The use of BTN pulse shaping filters allows for higher robustness to CFO as compared to base line pulse shaping filer know as root-raised cosine filters for different environments conditions. Simulation results are presented considering realistic air-to-ground propagation conditions generated from the commercial Wireless InSite raytracing radio propagation software.

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On-Demand Ultra-Dense Cloud Drone Networks: Opportunities, Challenges and Benefits

Cited by 51 publications

References 14 publications

UAV‐enabled computation migration for complex missions: A reinforcement learning approach

UAV‐enabled computation migration for complex missions: A reinforcement learning approach

Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things

Impact of CFO on Low Latency-Enabled UAV Using "Better Than Nyquist" Pulse Shaping in GFDM

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