Capacity Analysis of UAV Communications: Cases of Random Trajectories

Yuan, Xin; Feng, Zhiyong; Xu, Wenjun; Ni, Wei; Zhang, Andrew; Wei, Zhiqing; Liu, Ren Ping

doi:10.1109/tvt.2018.2829726

Cited by 76 publications

(66 citation statements)

References 35 publications

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“…The parameters used in the simulation are [36]- [38]: α e = 1.5, α 0 = 2, a = 11.95, b = 0.136, K max = 15dB, K o = 5dB, A = (4πf c /c) −2 , f c = 2.0Ghz, σ 2 n = −174dBm, P tx = −43dBm, and R min = 1bits/s/Hz unless otherwise stated. For ease of expositions, we directly translate our results from the time domain with T = 24.5s / N = 2450, and T = 23.5s / N = 2350 to the horizontal distance and altitude perspectives (meters), respectively, and the velocity of the UAV is ν = 20m/s for both cases.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…2 is the Rician K-factor and I 0 (•) is the zero-order modified Bessel function of the first kind. According to [37], [38], the Rician K-factor varies according to the elevation angle because larger elevation angle results in higher LOS gain and lower elevation angle results in lower LOS gain. Therefore, the angle-dependent Rician K-factor at time n is:…”

Section: Random Variable That Follows a Non-central Chi-square Probabmentioning

confidence: 99%

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Robust Non-Orthogonal Multiple Access for Aerial and Ground Users

Leow

Navaie

Ding

2020

IEEE Trans. Wireless Commun.

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In this paper, we consider a downlink wireless communication system with the coexistence of ground user (GU) and mobile aerial user (AU). Existing solutions rely on orthogonal multiple access (OMA) to support these users, however, OMA is unable to provide the best rate and outage performance because its spectral efficiency is limited by the users' channel conditions and rate requirements. Thus, we propose an aerialground non-orthogonal multiple access (AG-NOMA) scheme that pairs the GU and AU for data and control links, respectively. Unlike terrestrial non-orthgonal multiple access (NOMA), the key idea of AG-NOMA is to exploit the asymmetric features of the channels and rate demands of the GU and AU in the downlink communication. Based on these opportunities, we investigate the maximum achievable GU rate over a time-varying wireless channel while satisfying the AU Quality-of-Service (QoS) requirement with perfect and partial channel state information (CSI). For perfect CSI, we derive the optimal successive interference cancellation (SIC) policy, power allocation, GU rate, and feasibility conditions in closed-form expressions. For partial CSI, we also derive the suboptimal SIC policy and power allocation in closed-form expressions, and further discussed a tradeoff between the achievable rate and reliability. This tradeoff depends on the system parameters, and thus we have suggested some appropriate parameters based on theoretical support and standard requirements to strike a balance between rate and reliability. Our simulation results show that AG-NOMA scheme with perfect and partial CSI can achieve up to +99% GU rate-improvement as compared to OMA and provide a more sustainable rate-improvement and/or lower outage probability than terrestrial NOMA scheme.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Random Variable That Follows a Non-central Chi-square Probabmentioning

confidence: 99%

Robust Non-Orthogonal Multiple Access for Aerial and Ground Users

Leow

Navaie

Ding

2020

IEEE Trans. Wireless Commun.

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“…We control a UAV to randomly fly around the receiver and sweep the velocity from 0 m/s to 10 m/s. Note that flying in a random trajectory is a more general setting to analyze the communication performance [23]. We test RSSI difference to indicate the time feature and CSI similarity to indicate the feature of frequency-selective fading, respectively.…”

Section: B Observationmentioning

confidence: 99%

State-Aware Rate Adaptation for UAVs by Incorporating On-Board Sensors

Wang

Yang

et al. 2020

IEEE Trans. Veh. Technol.

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Nowadays unmanned aerial vehicles (UAVs) are being widely applied to a wealth of civil and military applications. Robust and high-throughput wireless communication is the crux of these UAV applications. Yet, air-to-ground links suffer from time-varying channels induced by the agile mobility and dynamic environments. Rate adaptation algorithms are generally used to choose the optimal data rate based on the current channel conditions. State-of-the-art approaches leverage physical layer information for rate adaptation, and they work well under certain conditions. However, the above protocols still have limitation under constantly changing flight states and environments for airto-ground links. To solve this problem, we propose StateRate, a state-optimized rate adaptation algorithm that fully exploits the characteristics of UAV systems using a hybrid deep learning model. The key observation is that the rate adaptation strategy needs to be adjusted according to motion-dependent channel models, which can be reflected by flight states. In this work, the rate adaptation protocol is enhanced with the help of the on-board sensors in UAVs. To make full use of the sensor data, we introduce a learning-based prediction module by leveraging the internal state to dynamically store temporal features under variable flight states. We also present an online learning algorithm by employing the pre-trained model that adapts the rate adaptation algorithm to different environments. We implement our algorithm on a commercial UAV platform and evaluate it in various environments. The results demonstrate that our system outperforms the best-known rate adaptation algorithm up to 53% in terms of throughput when the velocity is 2-6 m/s.

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“…However, all of those prior works considered limited UAV communication scenarios or environments. Specifically, only the path loss is used for channels without fading in [20], [21], [29]- [31], [33], or the fact that the LoS probability can be different according to the locations of the UAV was not considered in [22], [32]. In addition, the works in [23]- [28] considered the different channel fadings depending on the LoS…”

Section: Introductionmentioning

confidence: 99%

Impact of an Interfering Node on Unmanned Aerial Vehicle Communications

Kim

Lee

2019

IEEE Trans. Veh. Technol.

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Unlike terrestrial communications, unmanned aerial vehicle (UAV) communications have some advantages such as the line-of-sight (LoS) environment and flexible mobility. However, the interference will be still inevitable. In this paper, we analyze the effect of an interfering node on the UAV communications by considering the LoS probability and different channel fading for LoS and non-line-of-sight (NLoS) links, which are affected by horizontal and vertical distances of the communication link. We then derive a closed-form outage probability in the presence of an interfering node for all the possible scenarios and environments of main and interference links. After discussing the impacts of transmitting and interfering node parameters on the outage probability, we show the existence of the optimal height of the UAV that minimizes the outage probability. We also show the NLoS environment can be better than the LoS environment if the average received power of the interference is more dominant than that of the transmitting signal on UAV communications. Finally, we analyze the network outage probability for the case of multiple interfering nodes using stochastic geometry and the outage probability of the single interfering node case, and show the effect of the interfering node density on the optimal height of the UAV. Index Terms-Unmanned aerial vehicle, interfering node, airto-air channel, line-of-sight probability, outage probability P m , β I (ℓ I ) = ℓ −αI(ℓI) I P I .(2) 1 Note that the result of this paper can be readily extended for the multiple interfering nodes case as presented in Section IV. However, the analysis results will be complicated and give fewer insights. In addition, the communication performance is generally determined by one critical interfering node, especially in low outage probability region [36]. Therefore, we focus on the one interfering node case in this work, but the performance for the multiple interfering nodes case is also presented in simulation results of Section V.

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Capacity Analysis of UAV Communications: Cases of Random Trajectories

Cited by 76 publications

References 35 publications

Robust Non-Orthogonal Multiple Access for Aerial and Ground Users

Robust Non-Orthogonal Multiple Access for Aerial and Ground Users

State-Aware Rate Adaptation for UAVs by Incorporating On-Board Sensors

Impact of an Interfering Node on Unmanned Aerial Vehicle Communications

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