UAV Based Satellite-Terrestrial Systems With Hardware Impairment and Imperfect SIC: Performance Analysis of User Pairs

Nguyen, Ngoc-Long; Le, Si-Phu; Le, Anh‐Tu; Nguyen, Nhan Duc; Do, Dinh‐Thuan; Vozňák, Miroslav

doi:10.1109/access.2021.3107253

Cited by 7 publications

(4 citation statements)

References 47 publications

(37 reference statements)

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“…By expressing the relevant functions in (21) into Meijer G-function using the above equalities in (22), ( 24) and ( 25), P D1 is expressed as (26) We apply the following (27) to solve integrals (26) on the next top page in which H m,n:s,t:i,j p,q:u,v:e,f (•) stands for the extended generalized bivariate Fox H-function (EGBFHF) in [48]. This function can be conveniently evaluated using mathematical software such as Mathematica [51, Table I] and Matlab [52, Appx A].…”

Section: Appendix a Proof Of Propositionmentioning

confidence: 99%

“…In addition, the transmit/receive signal is always accompanied by a little degree of unaccounted-for distortion brought on by residual hardware noise (RHN) [21]. In [22], NOMAassisted satellite communication with UAV relaying is investigated. In addition, the author considered a real-life scenario as imperfect hardware issues in UAV relaying and ground users.…”

Section: Introductionmentioning

confidence: 99%

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Performance Prediction in UAV-Terrestrial Networks With Hardware Noise

Minh,

Le,

et al. 2023

IEEE Access

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To enhance the service quality of the unmanned aerial vehicle (UAV), the UAV-aided Internet of Things (IoT) systems could deploy a Deep Neural Network (DNN) for performance prediction for the users. Non-orthogonal multiple access (NOMA) is applied to such networks in order to improve spectrum efficiency, and results in improved quality of service at the ground users under the mobility of UAV. The outage and ergodic capacity requirements of the IoT users may not be satisfied due to some imperfect system parameters such as hardware noise. A DNN-based algorithm for performance prediction and the design of multiple antennas at the UAV under hardware noise is proposed. In this DNN-based UAV-NOMA, the central controller (server) collects system parameters periodically based on observing the state of IoT system and performs adjustments to the dynamic environment. The closed-form expressions for the outage probability and the ergodic capacity are derived to evaluate the performance of a group of IoT users. Our numerical results demonstrate that: i) In contrast to the traditional UAV-NOMA system, the UAV cannot know the performance at each IoT user in order to adjust the parameters (i.e. power allocation factors) before transmitting the signals to the devices; while the proposed DNN-based IoT system is capable of predicting the performance; ii) The performance of the IoT users can be significantly improved by integrating more antennas at the UAV and limiting levels of hardware noise; iii) By designing NOMA, the UAV-NOMA-based IoT system can increase the throughput to the tune of 40% compared with the benchmark (the orthogonal multiple access (OMA)-based IoT).INDEX TERMS deep neural network, non-orthogonal multiple access (NOMA), line-of-sight (LoS), unmanned aerial vehicles (UAVs), internet-of-things (IoT), hardware noise.

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Section: Appendix a Proof Of Propositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Prediction in UAV-Terrestrial Networks With Hardware Noise

Minh,

Le,

et al. 2023

IEEE Access

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“…( 3)]. The authors also considered a UAV model with three-dimensional Cartesian coordinates (x, y, z), shown in [42] (Fig. 1), positioning the UAV over the z-axis (0, 0, z) and two-devices over the xaxis (x, 0, 0) or y-axis (0, y, 0).…”

Section: B Approaching K-means Algorithm For Uav Positioningmentioning

confidence: 99%

Approaching K-Means for Multiantenna UAV Positioning in Combination With a Max-SIC-Min-Rate Framework to Enable Aerial IoT Networks

2022

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In long-range wireless communication networks, the fading channels described in channel state information are strongly related to distance and the path loss exponent and represent a major challenge in delivering the performance required to support emerging applications. Conveniently, multiple antennas and cooperative relays are efficient solutions that can combat fading channels, thereby improving networking capacity This article has been accepted for publication in IEEE Access.

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“…Therefore, it is of great importance to develop high-speed maritime networks to improve the onboard user experience. As a result, maritime communications have garnered substantial interest in the recent past, where the primary purpose is to enhance the broadband network coverage for the terrestrial users with the aid of unmanned aerial vehicles (UAVs), that can serve as aerial base stations and relays [2], [3].…”

Section: Introductionmentioning

confidence: 99%

UAVs-Enabled Maritime Communications: Opportunities and Challenges

Akhtar¹,

Saeed²

2022

Preprint

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The next generation of wireless communication systems will integrate terrestrial and non-terrestrial networks targeting to cover the undercovered regions, especially connecting the marine activities. Unmanned aerial vehicles (UAVs) based connectivity solutions offer significant advances to support the conventional terrestrial networks. However, the use of UAVs for maritime communication is still an unexplored area of research. Therefore, this paper highlights different aspects of UAV-based maritime communication, including the basic architecture, various channel characteristics, and use cases. The article, afterward, discusses several open research problems such as mobility management, trajectory optimization, interference management, and beamforming.

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UAV Based Satellite-Terrestrial Systems With Hardware Impairment and Imperfect SIC: Performance Analysis of User Pairs

Cited by 7 publications

References 47 publications

Performance Prediction in UAV-Terrestrial Networks With Hardware Noise

Performance Prediction in UAV-Terrestrial Networks With Hardware Noise

Approaching K-Means for Multiantenna UAV Positioning in Combination With a Max-SIC-Min-Rate Framework to Enable Aerial IoT Networks

UAVs-Enabled Maritime Communications: Opportunities and Challenges

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