A simplified stochastic model for the aeronautical mobile radio channel

Elnoubi, S.

doi:10.1109/vetec.1992.245268

Cited by 37 publications

(15 citation statements)

References 8 publications

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“…In [95], the stochastic model was designed for characterizing the AG propagation in terms of transmission coefficients assuming that the quadrature components reflected from the ground surface can be modeled as a zero-mean Gaussian process. Also, Doppler spectrum analysis was performed for the diffuse multipath components.…”

Section: B Stochastic Channel Modelmentioning

confidence: 99%

“…• Characterization of multipath components can be done with both numerical [95]- [96] and empirical analysis [13]- [15] Geometry-based stochastic • 3D channel characterization is possible with less environmental parameters to study the channel state information in UAV propagation for e.g. angular information due to spatial-temporal variations without considering non-stationarity in the UAV channel [16], [18] • Accuracy is dependent on the distribution of scatters confined in a target area of specific shapes…”

Section: Vmentioning

confidence: 99%

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A Survey of Channel Modeling for UAV Communications

Khuwaja

Chen

Zhao

et al. 2018

IEEE Commun. Surv. Tutorials

689

346

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Unmanned aerial vehicles (UAVs) have gained great interest for rapid deployment in both civil and military applications. UAV communication has its own distinctive channel characteristics compared with widely used cellular and satellite systems. Thus, accurate channel characterization is crucial for the performance optimization and design of efficient UAV communication systems. However, several challenges exist in UAV channel modeling. For example, propagation characteristics of UAV channels are still less explored for spatial and temporal variations in non-stationary channels. Also, airframe shadowing has not yet been investigated for small size rotary UAVs. This paper provides an extensive survey on the measurement campaigns launched for UAV channel modeling using low altitude platforms and discusses various channel characterization efforts. We also review the contemporary perspective of UAV channel modeling approaches and outline some future research challenges in this domain.

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Section: B Stochastic Channel Modelmentioning

confidence: 99%

Section: Vmentioning

confidence: 99%

A Survey of Channel Modeling for UAV Communications

Khuwaja

Chen

Zhao

et al. 2018

IEEE Commun. Surv. Tutorials

689

346

View full text Add to dashboard Cite

show abstract

“…The dynamic nature of the aircraft motion and the rapidly changing environment generate varying channel characteristics that are categorized by different types of fading, Doppler Spread and system delays [132], [133], [134]. The take-off and landing speeds are in the range of 100-170 nautical miles/h while the other phases of flight will have a traveling speed of 600 nautical miles/h [135].…”

Section: A Challenges In Channel Modelingmentioning

confidence: 99%

Cognitive Radio for Aeronautical Communications: A Survey

et al. 2016

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Novel Air Traffic Management (ATM) strategies are proposed through the Next Generation Air Transportation (NextGen) and Single European Sky for ATM Research (SESAR) projects to improve the capacity of the airspace and to meet the demands of the future air traffic. The implementation of the proposed solutions leads to increasing use of wireless data for aeronautical communications. Another emerging trend is the unmanned aerial vehicles. The unmanned aerial systems (UASs) need reliable wireless data link and dedicated spectrum allocation for its operation. On-board broadband connectivity also needs dedicated spectrum to satisfy the quality of service (QoS) requirements of the users. With the growing demand, the aeronautical spectrum is expected to be congested. However, the studies revealed that the aeronautical spectrum is underutilized due to the static spectrum allocation strategy. The aeronautical communication systems such as air-air and airground communication systems, inflight infotainment systems, wireless avionics intra-communications (WAIC), and UAS can benefit significantly from the introduction of cognitive radio based transmission schemes. This article summarizes the current trends in aeronautical spectrum management followed by the major applications and contributions of cognitive radio in solving the spectrum scarcity crisis in the aeronautical domain. Also, to cope with the evolving technological advancement, researchers have prioritized the issues in the case of cognitive radio that needs to be addressed depending on the domain of operation. The proposed cognitive aeronautical communication systems should also be compliant with the Aeronautical Radio Incorporated (AR-INC) and Aerospace Recommended Practice (ARP) standards. An overview of these standards and the challenges that need immediate attention to make the solution feasible for a largescale operation, along with the future avenues of research is also furnished.

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“…ϕ aL is the lowest angle and ϕ aH is the highest angle of arrival signals from the aircraft antenna to the ground base station antenna. In [12], a beamwidth of Λ ≤ 360…”

Section: Dopplermentioning

confidence: 99%

On the use of spatial modulation in aeronautical communications

Raddadi

Thomas

Poulliat

et al. 2016

2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. Abstract-In this work, we apply Spatial Modulation (SM) to in the so-called spectrally shaped SC-OFDM (SS SC-OFDM) at the transmitter. The idea behind SS SC-OFDM lies in applying a frequency window between the Fast Fourier Transform (FFT) and the Inverse Fast Fourier Transform (IFFT) in order to lower the side-lobes of the resulting time domain generated waveform. Thus, we can avoid the peak-to-average power ratio (PAPR) problem encountered in multicarrier systems. At the receiver, a reduced-rank (rr) Least Square (LS) channel estimation and a combining Minimum Mean Square Error (MMSE) equalization are used to retrieve the transmitted block of information bits, considering two flight scenarios for different environments.

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A simplified stochastic model for the aeronautical mobile radio channel

Cited by 37 publications

References 8 publications

A Survey of Channel Modeling for UAV Communications

A Survey of Channel Modeling for UAV Communications

Cognitive Radio for Aeronautical Communications: A Survey

On the use of spatial modulation in aeronautical communications

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