Orthogonal frequency division multiplexing and channel models for payload communications of unmanned aerial systems

Vahidi, Vahid; Saberinia, E.

doi:10.1109/icuas.2016.7502656

Cited by 29 publications

(23 citation statements)

References 8 publications

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“…Apart from measurement campaigns for AG propagation channel modeling, some simulation based channel characterizations are also available in the literature, where the real time environmental scenarios are imitated using computer simulations. Simulations in urban/suburban areas were performed in [45], [85], [87], [92]. The antenna considered in these environments was omni-directional.…”

Section: F Simulations For Channel Characterizationmentioning

confidence: 99%

“…As previously noted, the most common small scale fading distribution for AG propagation is the Ricean. As in terrestrial channels, for the NLOS case, the Rayleigh fading distribution typically provides a better fit [30], [34], [42], [45], [47], [93], [115], and of course, other distributions such as the Nakagamim and Weibull distributions might also be employed. Small scale fading rates depend upon velocity, and these rates are proportional to the Doppler spreads of the MPCs [43], [46], [47], [50], [57], [57].…”

Section: Small Scale Fading Modelsmentioning

confidence: 99%

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A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

Khawaja

Güvenç

Matolak

et al. 2019

IEEE Commun. Surv. Tutorials

547

206

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In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, ease of availability, and ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail when compared to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios.

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Section: F Simulations For Channel Characterizationmentioning

confidence: 99%

Section: Small Scale Fading Modelsmentioning

confidence: 99%

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

Khawaja

Güvenç

Matolak

et al. 2019

IEEE Commun. Surv. Tutorials

547

206

View full text Add to dashboard Cite

show abstract

“…In each run of the simulation, we calculate and plot the mean square error (MSE) for the channel parameters and bit error rate (BER) of data transmission versus SNR. For the assessment of the CE schemes for the transmission of the hyperspectral data, the overall accuracy (OA) of classification criterion is utilized, which is expressed by 28 E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 2 8 ; 1 1 6 ; 1 4…”

Section: Performance Evaluation Criterionsmentioning

confidence: 99%

“…Since orthogonal frequency division multiplexing (OFDM) has high-bandwidth efficiency and is robust to multipath effect, it has been applied for wideband terrestrial communications and can be a feasible candidate for UAV communications as well. 4 On the other hand, the employment of massive multiple-input multiple-output (MIMO) utilizes channel diversity and increases the effective signal-to-noise ratio (SNR). However, using the massive MIMO-OFDM for UAV communications is challenging since the movement of UAVs imposes high Doppler shifts and makes the communication channel to be doubly selective (DS), i.e., both time and frequency selective, and therefore, destroys the orthogonality between the OFDM subcarriers and causes intercarrier interference (ICI).…”

Section: Introductionmentioning

confidence: 99%

“…However, using the massive MIMO-OFDM for UAV communications is challenging since the movement of UAVs imposes high Doppler shifts and makes the communication channel to be doubly selective (DS), i.e., both time and frequency selective, and therefore, destroys the orthogonality between the OFDM subcarriers and causes intercarrier interference (ICI). 4 In order to demodulate the MIMO-OFDM signal at the receiver and mitigate ICI, the knowledge of channel impulse response (CIR) is required at the receiver, and therefore, channel estimation (CE) is essential for MIMO-OFDM-based UAV communications. In addition to the high Doppler shift for UAV communications, the experimental results indicate that the tap delay line channel models for several vehicular communication systems are long and sparse.…”

Section: Introductionmentioning

confidence: 99%

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Elevation and azimuth-aided channel estimation scheme for airborne hyperspectral data transmission

Vahidi

Saberinia

2018

J. Appl. Rem. Sens.

Self Cite

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A channel-estimation (CE) scheme is proposed to estimate the complex amplitude, Doppler shift, angle-of-departure, and angle-of-arrival of the channel taps for sparse and doubly selective channels for hyperspectral image transmission from unmanned aircraft vehicles (UAVs) to ground stations. The proposed method is dubbed as compressed-sensing joint parameter estimation (CS-JPE) and finds the channel parameters matrix by employing a compressed-sensing (CS)-based method. Afterward, a modified version of the joint parameter estimation (JPE) is proposed as CS-JPE and is dubbed as M-CS-JPE, which employs the elevation-azimuth angles of the line-of-sight channel tap to estimate the channel parameters with higher accuracy and lower computational complexity compared to the CS-JPE scheme. For higher accuracy of the M-CS-JPE, an elevation-azimuth angle estimation is proposed and is dubbed as fractal-structurearray since it uses a fractal structure for the placement of the UAV antennas. The performance of the CE methods is appraised by simulating transmission of AVIRIS hyperspectral data via the communication channel and evaluating their accuracy for the classification after demodulation. Compared to the least-square method, the simulation results indicate up to 30-dB figure of merit in the bit-error-rate and 10 times improvement in the hyperspectral image classification fidelity.

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A Survey of Air‐to‐Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

Khawaja¹,

Güvenç²,

Matolak³

et al. 2020

UAV Communications for 5G and Beyond

101

169

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Orthogonal frequency division multiplexing and channel models for payload communications of unmanned aerial systems

Cited by 29 publications

References 8 publications

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

Elevation and azimuth-aided channel estimation scheme for airborne hyperspectral data transmission

A Survey of Air‐to‐Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

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