Effects of Fuselage Scattering and Posture on UAV Channel

Hua, Boyu; Zhu, Qiuming; Wang, Cheng-Xiang; Ni, Haoran; Masuda, Kai; Zhou, Tongtong; Zhong, Weizhi; Chang, Hengtai

doi:10.1007/978-981-19-0390-8_150

Cited by 2 publications

(5 citation statements)

References 10 publications

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“…15, respectively. It can be found that the generated results of proposed model are well consistent with the measured ones and outperform the ones in [78]. Taking the CDF as an example, the Kolmogorov-Smirnov (KS) test is adopted to quantitatively analyze the CDFs.…”

Section: B Validation Of Proposed Channel Modelmentioning

confidence: 57%

“…14, the maximum absolute difference between the simulated CDF in [78] and measured one is 0.125. The maximum absolute difference between the simulated CDF of proposed model and measured one is only 0.043, which is much lower than the critical value 0.24 and outperform the one in [78]. Note that the proposed method consumes about 1.4 s to generate the channel parameters in the expressions of statistical properties for a 10-cluster channel after the digital map is determined, and the stochastic method in [78] would consume hundreds of milliseconds to initialize the random simulation scenario and generate the needed channel parameters.…”

Section: B Validation Of Proposed Channel Modelmentioning

confidence: 86%

“…In Fig. 14, the maximum absolute difference between the simulated CDF in [78] and measured one is 0.125. The maximum absolute difference between the simulated CDF of proposed model and measured one is only 0.043, which is much lower than the critical value 0.24 and outperform the one in [78].…”

Section: B Validation Of Proposed Channel Modelmentioning

confidence: 91%

“…13, which achieves a good agreement with each other. Moreover, the UAV channel model in [78] considers the 3D posture of UAV but all channel parameters are generated by the stochastic method. To demonstrate the effectiveness of proposed model, the generated CDF and LCR of proposed model are compared with the ones in [78] and the measurement results in Fig.…”

Section: B Validation Of Proposed Channel Modelmentioning

confidence: 99%

See 3 more Smart Citations

Machine-Learning-Based 3-D Channel Modeling for U2V mmWave Communications

Masuda

Zhu

Song

et al. 2022

IEEE Internet Things J.

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Unmanned aerial vehicle (UAV) millimeter wave (mmWave) technologies can provide flexible link and high data rate for future communication networks. By considering the new features of three-dimensional (3D) scattering space, 3D velocity, 3D antenna array, and especially 3D rotations, a machine learning (ML) integrated UAV-to-Vehicle (U2V) mmWave channel model is proposed. Meanwhile, a ML-based network for channel parameter calculation and generation is developed. The deterministic parameters are calculated based on the simplified geometry information, while the random ones are generated by the back propagation based neural network (BPNN) and generative adversarial network (GAN), where the training data set is obtained from massive ray-tracing (RT) simulations. Moreover, theoretical expressions of channel statistical properties, i.e., power delay profile (PDP), autocorrelation function (ACF), Doppler power spectrum density (DPSD), and cross-correlation function (CCF) are derived and analyzed. Finally, the U2V mmWave channel is generated under a typical urban scenario at 28 GHz. The generated PDP and DPSD show good agreement with RT-based results, which validates the effectiveness of proposed method. Moreover, the impact of 3D rotations, which has rarely been reported in previous works, can be observed in the generated CCF and ACF, which are also consistent with the theoretical and measurement results.

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Section: B Validation Of Proposed Channel Modelmentioning

confidence: 57%

Section: B Validation Of Proposed Channel Modelmentioning

confidence: 86%

Section: B Validation Of Proposed Channel Modelmentioning

confidence: 91%

Section: B Validation Of Proposed Channel Modelmentioning

confidence: 99%

See 2 more Smart Citations

Machine-Learning-Based 3-D Channel Modeling for U2V mmWave Communications

Masuda

Zhu

Song

et al. 2022

IEEE Internet Things J.

Self Cite

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“…Recently, the 3D rotation and posture have been considered in Ref. [18], where the posture of the UAV in 3D arbitrary trajectories is studied. Although, several other papers focussed on movements of transceivers [19,20], 3D scattering environment [21], 3D arbitrary trajectories [21], and 3D antenna arrays [22].…”

Section: Introductionmentioning

confidence: 99%

3D geometry‐based UAV to vehicle multiple‐input multiple‐output channel model incorporating Unmanned aerial vehicle heaving motion

Ahmed,

Hua,

Ali

et al. 2023

IET Microwaves Antenna & Prop

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Unmanned aerial vehicles (UAVs) play a vital role in the beyond fifth‐generation (B5G) and sixth‐generation (6G) wireless communication scenarios. This paper proposes a non‐stationary three‐dimensional (3D) geometry‐based multiple‐input multiple‐output channel model for UAV to vehicle (U2V) communications considering UAV heaving motion. The proposed model incorporates the effects of UAV heaving along with the rotation, acceleration, and 3D velocity variation for matching the realistic U2V communication scenarios. The computation methods for time variant channel parameters such as Doppler frequencies, angle of arrival, and angle of departure are given and analysed. The theoretical and statistical properties, including spatial temporal correlation function, TCF, level crossing rate, average fade duration, and Doppler power spectral density are derived and analysed aiming to evaluate the proposed model. Simulation results show that the output statistical characteristics agree with the hypothetical and simulated outcomes, which shows the validity of both the channel model and theoretical derivations.

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Effects of Fuselage Scattering and Posture on UAV Channel

Cited by 2 publications

References 10 publications

Machine-Learning-Based 3-D Channel Modeling for U2V mmWave Communications

Machine-Learning-Based 3-D Channel Modeling for U2V mmWave Communications

3D geometry‐based UAV to vehicle multiple‐input multiple‐output channel model incorporating Unmanned aerial vehicle heaving motion

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