Coverage Analysis for Energy-Harvesting UAV-Assisted mmWave Cellular Networks

Abstract

In this paper, we jointly consider the downlink simultaneous wireless information and power transfer (SWIPT) and uplink information transmission in unmanned aerial vehicle (UAV)-assisted millimeter wave (mmWave) cellular networks, in which the user equipment (UE) locations are modeled using Poisson cluster processes (e.g., Thomas cluster processes or Matérn cluster processes). Distinguishing features of mmWave communications, such as different path loss models for line-of-sight (LOS) and non-LOS (NLOS) links and directional transmissions are taken into account. In the downlink phase, the association probability, and energy coverages of different tier UAVs and ground base stations (GBSs) are investigated. Moreover, we define a successful transmission probability to jointly present the energy and signal-to-interference-plus-noise ratio (SINR) coverages and provide general expressions. In the uplink phase, we consider the scenario that each UAV receives information from its own cluster member UEs. We determine the Laplace transform of the interference components and characterize the uplink SINR coverage. In addition, we formulate the average uplink throughput, with the goal to identify the optimal time division multiplexing between the donwlink and uplink phases. Through numerical results we investigate the impact of key system parameters on the performance. We show that the network performance is improved when the cluster size becomes smaller. In addition, we analyze the optimal height of UAVs, optimal power splitting value and optimal time division multiplexing that maximizes the network performance.The authors are with the [7]. In conventional wireless power transfer (WPT) systems, energy transmitters are deployed at fixed locations, and therefore due to the RF signal propagation over potentially long distances, such systems can suffer from low end-to-end energy transfer efficiency [4]. In general, RF WPT is considered in the context of two key application scenarios, namely simultaneous wireless information and power transfer (SWIPT) and wireless powered communication networks (WPCNs). SWIPT explores the dual use of microwave signals to achieve WPT and wireless information transfer (WIT) both in downlink direction [8], while downlink WPT and uplink WIT are performed in WPCNs [9]. Unmanned aerial vehicles (UAVs) have emerged as key enablers of seamless wireless connectivity in diverse scenarios such as large-scale temporary events, military operations and disaster scenarios, and of capacity enhancement in the occasional demand of super dense base stations (BSs), and UAVs are anticipated to be part of future generation wireless networks [10][11][12][13]. More specifically, in order to take advantage of flexible deployment opportunities [12], and high possibility of line-of-sight (LoS) connections with a ground user equipment (UE) [14], BSs can be mounted on UAVs to support wireless connectivity and improve the performance of cellular networks [13]. The flexibility of UAV BSs allows them to adapt their locations ...

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“…The ground-to-air channel model is characterized by blockage, that divides the links between the terrestrial BSs on the ground and the reference UAV-DU between LoS and NLoS. Thus, two different pathloss functions for the LoS and NLoS cases can be identified, leading to: [20] −69.7dB [16], −68dB [17] the sub-6 GHz band these values have been standardized by the 3rd Generation Partnership Project (3GPP) in [18] for different UAV heights. At mmWave band, since no standardized results exist, the values used in recent works on UAV communication differ [16], [17].…”

SIR Analysis in 3D UAV Networks: A Stochastic Geometry Approach

“…In [8], PPP is incorporated in the coverage probability (CP) analysis of a UAV network. The authors in [9] studied the performance of a multi-tier network, including UAVs, whose locations modeled as an HPPP on the Euclidean plane. However, the HPPP cannot be used in modeling finite UAV networks with a given number of UAVs.…”

Multi-UAV Assisted Multi-Tier Millimeter-Wave Cellular Networks for Hotspots With 2-Tier and 4-Tier Network Association

“…With 3D antenna gain model, authors proposed transmission jamming strategy to improve the network performance. Unlike the work [15], in works [16]- [17] both aerial U-BSs and G-BSs were modeled as independent PPPs. The locations of GUE were modeled as PCP whose parent process was projections of aerial UAVs.…”

“…In works [18]- [19], WQ Yi et al also investigated the UAV-assisted mm-Wave HetNets by specially considering the coupling between U-BSs/G-BSs and UEs. Moreover, the spatial models of the network elements' locations are completely same as the ones in [16]- [17]. The main contributions of the works [18]- [19] are that the authors firstly proposed a novel 3D analytical framework for UAV-assisted cellular network by invoking a tractable 3D blockage model and a 3D sectored antenna pattern, in which the altitude of all transceivers and the cluster property of GUEs were jointly considered.…”

“…of L 2 is given by (42). The PDF of the path loss L 0,L (z 0 ) and L 0,N (z 0 ) are given by (17) and (23), respectively.…”