Impact of UAV Wobbling on the Air-to-Ground Wireless Channel

Banagar, Morteza; Dhillon, Harpreet S.; Molisch, Andreas F.

doi:10.1109/tvt.2020.3026975

Cited by 56 publications

(32 citation statements)

References 20 publications

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“…The extensive measurements in [36] and [37] show that the AG link can be modeled as a frequency-selective Rician fading channel with a K factor that ranges from 2 to 20 dB. The channel ordering is assumed to remain fixed for the coherence time of the channel, which can be more than 5 ms [38], which allows a reasonable CSI exchange frequency.…”

Section: System and Channel Modelsmentioning

confidence: 99%

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NOMA Receiver Design for Delay-Sensitive Systems

Assaf

Al-Dweik

Moursi

et al. 2021

IEEE Systems Journal

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Section: System and Channel Modelsmentioning

confidence: 99%

“…In order to evaluate the average BER of the two users in N = 2 system over Rician channel, (37) and (38) are utilized as follows:…”

Section: Appendix Average Ber Over Rician Fading Channelmentioning

confidence: 99%

NOMA Receiver Design for Delay-Sensitive Systems

Assaf

Al-Dweik

Moursi

et al. 2021

IEEE Systems Journal

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“…Nevertheless, UAVs are typically moving causing fast variation to the channel phase, which complicates the overall phase processing. Channel variations are expected even when the UAV is hovering due to the UAV wobbling [40], and thus, the coherence time of the channel will be generally short. Therefore, the IRS might be provided with outdated phase information.…”

Section: Introductionmentioning

confidence: 99%

IRS-Assisted UAV Communications with Imperfect Phase Compensation

Al-Dweik¹,

Al‐Jarrah²,

Alsusa³

et al. 2020

Preprint

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<div>This work presents a performance analysis on unmanned aerial vehicles (UAVs) assisted wireless</div><div>communications systems, where one of the UAVs supports intelligent reflecting surfaces (IRS). As the</div><div>estimation and compensation of the end-to-end phase for each propagation path is prone to errors, imperfect</div><div>phase compensation at the IRS is taken into consideration. The performance is derived in terms of symbol</div><div>error rate (SER) and outage probability, where the phase error is modeled using the von Mises distribution.</div><div>The air-to-air (A2A) channel for</div><div>each propagation path is modeled as a single dominant line-of-sight (LoS) component, and the results are</div><div>compared to the Rician channel model. The obtained results reveal that the considered A2A model can be</div><div>used to accurately represent the A2A channel with Rician fading.</div>

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“…Although estimation of wind disturbances has been extensively studied in flight control to mitigate wind effect [8], [9], UAV jitter/vibration, i.e., the unintended high-frequency change of UAV attitude, is still non-negligible, especially for multi-rotor UAVs. For wireless communication system deployed in UAV platform, jittering effects could severely deteriorate the quality of wireless channel when carrier frequency is high, as the path variation caused by UAV jitter is significant compared with the wavelength [10]. However, current studies on UAV channel mainly focus on propagation attenuation, shadowing, root mean square (RMS) delay spread (DS), over lower frequency band, typically ranging from 0.8 GHz to 8 GHz [10], [11].…”

Section: Introductionmentioning

confidence: 99%

“…For wireless communication system deployed in UAV platform, jittering effects could severely deteriorate the quality of wireless channel when carrier frequency is high, as the path variation caused by UAV jitter is significant compared with the wavelength [10]. However, current studies on UAV channel mainly focus on propagation attenuation, shadowing, root mean square (RMS) delay spread (DS), over lower frequency band, typically ranging from 0.8 GHz to 8 GHz [10], [11]. Since beamforming is the enabling technique for mmWave communications, spatial domain parameters, e.g., AoA and AoD, are more worthy of study than the aforementioned channel parameters in mmWave band.…”

Section: Introductionmentioning

confidence: 99%

Joint 3D Codebook Design and Beam Training for UAV Millimeter-Wave Communications

Wang

Wen

Chen

et al. 2019

2019 IEEE 30th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC Workshops)

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Jittering effects significantly degrade the performance of UAV communications especially in millimeterwave (mmWave) band. To investigate and mitigate the impacts of UAV jitter to mmWave communications, we firstly model UAV mmWave channel based on the geometric relationship between element antennas of the uniform planar arrays (UPAs) in receiver side and transmitter side, and we incorporate the jittering effects to our channel model through extracting the relationship between UAV attitude & position and angle of arrival (AoA) & angle of departure (AoD) of the UPAs. Then, based on the extracted relationship, we propose to utilize UAV navigation information to obtain a rough estimation of AoA and AoD, and we also analyze the impact of AoA and AoD estimation error to UAV beamforming.Finally, we propose a direction-constrained beam training scheme to refine the AoA/AoD estimation.Particularly, we construct a partially random sensing matrix to measure the channel within a narrow angle range that is centered at the aforementioned rough estimate of AoA/AoD. Numerical results show that our proposed UAV beam training scheme with navigation information is able to fast and accurately estimate the AoA/AoD fluctuation caused by UAV jitter.

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Impact of UAV Wobbling on the Air-to-Ground Wireless Channel

Cited by 56 publications

References 20 publications

NOMA Receiver Design for Delay-Sensitive Systems

NOMA Receiver Design for Delay-Sensitive Systems

IRS-Assisted UAV Communications with Imperfect Phase Compensation

Joint 3D Codebook Design and Beam Training for UAV Millimeter-Wave Communications

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