Modeling and Simulation for UAV Air-to-Ground mmWave Channels

Cheng, Lele; Zhu, Qiuming; Wang, Cheng-Xiang; Zhong, Weizhi; Hua, Boyu; Jiang, Shan

doi:10.23919/eucap48036.2020.9136077

Cited by 32 publications

(26 citation statements)

References 19 publications

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“…The authors in [9] developed a generative neural network to predict the mmWave link state and model statistical channel parameters between a UAV and a ground BS. An empirical propagation loss model is proposed in [13] based on an extensive measurement for UAV-to-UAV communications at 60 GHz, and a traditional ray tracing method is applied in [14] to build a geometry-based stochastic model for UAV-to-vehicle communications at 28 GHz. Furthermore, the received signal strength and delay spread of mmWave transmissions is analyzed in [15] to provide further details for A2G channels.…”

Section: A Related Workmentioning

confidence: 99%

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Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks

Zhang

Ferdowsi

Saad

et al. 2021

Preprint

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In this paper, a novel framework is proposed to perform data-driven air-to-ground channel estimation for millimeter wave (mmWave) communications in an unmanned aerial vehicle (UAV) wireless network.First, an effective channel estimation approach is developed to collect mmWave channel information, allowing each UAV to train a stand-alone channel model via a conditional generative adversarial network (CGAN) along each beamforming direction. Next, in order to expand the application scenarios of the trained channel model into a broader spatial-temporal domain, a cooperative framework, based on a distributed CGAN architecture, is developed, allowing each UAV to collaboratively learn the mmWave channel distribution in a fully-distributed manner. To guarantee an efficient learning process, necessary and sufficient conditions for the optimal UAV network topology that maximizes the learning rate for cooperative channel modeling are derived, and the optimal CGAN learning solution per UAV is subsequently characterized, based on the distributed network structure. Simulation results show that the proposed distributed CGAN approach is robust to the local training error at each UAV. Meanwhile, a larger airborne network size requires more communication resources per UAV to guarantee an efficient learning rate. The results also show that, compared with a stand-alone CGAN without information sharing and two other distributed schemes, namely: A multi-discriminator CGAN and a federatedlearning CGAN method, the proposed distributed CGAN approach yields a higher modeling accuracy while learning the environment, and it achieves a larger average data rate in the online performance of UAV downlink mmWave communications.

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Section: A Related Workmentioning

confidence: 99%

“…Given the fixed value of l max (G * ), the convergence rate will be constant, for any ordered set of ring structures. Therefore, the feasible solution leads to one identical outcome of the convergence time for (14), and it is the optimal result, as long as the UAV network has a ring structure.…”

Section: Appendix D Proof Of Propositionmentioning

confidence: 99%

Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks

Zhang

Ferdowsi

Saad

et al. 2021

Preprint

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show abstract

“…From the existing literatures, a lot of channel models have been studied in indoor or outdoor scenarios [1][2][3][4][5][6][7]. However, there are few studies for the inside of tower structures.…”

Section: Introductionmentioning

confidence: 99%

Channel Propagation Characteristics for the Communications Inside Tower Structure Buildings

Yin¹,

Xie²,

Hao³

et al. 2021

PIER M

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“…So far, most of mmWave channel models were mainly aimed at the land mobile communication scenarios [17,18], e.g., tunnel [19,20], microcellular environment [21], and high-voltage substation [22], etc. A few mmWave channel models involving UAV scenarios can be addressed in [23][24][25][26]. In [23,24], the authors used ray tracing (RT) simulated data to develop UAV mmWave channel models and analyze the channel parameters, i.e., received power, path delay, angle, etc.…”

Section: Introductionmentioning

confidence: 99%

“…In [25], a 3D non-stationary mmWave channel for UAV communication was proposed based on the geometry-based stochastic model (GBSM) method, but the flight velocity was constant and the receiving terminal was also fixed. Recently, the authors in [26] proposed a mmWave UAV channel model allowing 3D trajectories, but the rotation of 3D-shaped antenna array and the effect of beam-forming were not considered.…”

Section: Introductionmentioning

confidence: 99%

A Geometry-Based Beamforming Channel Model for UAV mmWave Communications

Masuda

Zhu

Song

et al. 2020

Sensors

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Considering the three-dimensional (3D) trajectory, 3D antenna array, and 3D beamforming of unmanned aerial vehicle (UAV), a novel non-stationary millimeter wave (mmWave) geometry-based stochastic model for UAV to vehicle communication channels is proposed. Based on the analysis results of measured and ray tracing simulation data of UAV mmWave communication links, the proposed parametric channel model is constructed by a line-of-sight path, a ground specular path, and two strongest single-bounce paths. Meanwhile, a new parameter computation method is also developed, which is divided into the deterministic (or geometry-based) part and the random (or empirical) part. The simulated power delay profile and power angle profile demonstrate that the statistical properties of proposed channel model are time-variant with respect to the scattering scenarios, positions and beam direction. Moreover, the simulation results of autocorrelation functions fit well with the theoretical ones as well as the measured ones.

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Modeling and Simulation for UAV Air-to-Ground mmWave Channels

Cited by 32 publications

References 19 publications

Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks

Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks

Channel Propagation Characteristics for the Communications Inside Tower Structure Buildings

A Geometry-Based Beamforming Channel Model for UAV mmWave Communications

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