Multi-Frequency Air-to-Ground Channel Measurements and Analysis for UAV Communication Systems

Cui, Zhuangzhuang; Briso-Rodríguez, César; Guan, Ke; Zhang, Zhong; Quitin, François

doi:10.1109/access.2020.2999659

Cited by 41 publications

(24 citation statements)

References 23 publications

(34 reference statements)

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“…In [21] a trajectory optimization in Rician fading channels for data harvesting is proposed. Cui et al [22] presents measurements for Air-to-Ground (A2G) channels across several frequencies. Liu et al [23] characterizes and develops a model for UAV Air-to-Air (A2A) channels with Low-Altitude based on field measurements.…”

Section: A Related Workmentioning

confidence: 99%

Increasing Reliability on UAV Fading Scenarios

et al. 2022

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Unmanned aerial vehicles (UAVs) are the next technology to be incorporated into a telecommunications network to improve command and control on a large scale in both lines of sight (LOS) and non-line of sight (NLOS) conditions. However, there is still room for improvement in terms of reliability. This paper investigates Constant Packet Combining (CPC) and Adaptive Packet Combining (APC) techniques applied to Unmanned Aerial Vehicle (UAV) communication in the presence of largescale fading, where the channels are subject to sudden degradation for long periods due to obstructions. We use Single Carrier (SC) Frequency Domain Equalization (FDE) combined with the Iterative Block Decision-Feedback Equalizer (IB-DFE) to handle command and control messages map for UAV use cases. We present closed-form equations for the equalization design and for the perfornance parameters such as Bit Error Rate (BER), the Packet Error Rate (PER), the throughput, the retransmissions amount, the goodput (the transmission rate without the retransmissions quantity), and the outage probability. Then, we analyze the system performance using correlated, independent, and equal channels. There is a trade-off between the overall available power, throughput, and reliability , for instance, more retransmissions result in higher reliability and power consumption and lower goodputs (effective data rates). CPC validates the transmission system and confirms the improvement in BER and PER parameters without energy efficiency optimization. The APC is appealing because it can reduce the number of retransmissions for all channels used with the advantage of meeting energy efficiency requirements by adapting the overall power to the scenario experienced by the UAV.

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Section: A Related Workmentioning

confidence: 99%

Increasing Reliability on UAV Fading Scenarios

et al. 2022

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“…For small-scale fading, most conventional stochastic models, including Rayleigh, Rician, Nakagami-m, Weibull fading models, etc., have been considered in the A2G scenario [60]. Among them, Rician fading has been widely assumed in the presence of a dominant LoS link.…”

Section: B Channel and Location Distribution Modelsmentioning

confidence: 99%

Physical Layer Security for UAV Communications in 5G and Beyond Networks

Wang,

Gao

et al. 2021

Preprint

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Due to its high mobility and flexible deployment, unmanned aerial vehicle (UAV) is drawing unprecedented interest in both military and civil applications to enable agile wireless communications and provide ubiquitous connectivity. Mainly operating in an open environment, UAV communications can benefit from dominant line-of-sight links; however, it on the other hand renders the UAVs more vulnerable to malicious eavesdropping or jamming attacks. Recently, physical layer security (PLS), which exploits the inherent randomness of the wireless channels for secure communications, has been introduced to UAV systems as an important complement to the conventional cryptographybased approaches. In this paper, a comprehensive survey on the current achievements of the UAV-aided wireless communications is conducted from the PLS perspective. We first introduce the basic concepts of UAV communications including the typical static/mobile deployment scenarios, the unique characteristics of air-to-ground channels, as well as various roles that a UAV may act when PLS is concerned. Then, we introduce the widely used secrecy performance metrics and start by reviewing the secrecy performance analysis and enhancing techniques for statically deployed UAV systems, and extend the discussion to a more general scenario where the UAVs' mobility is further exploited. For both cases, respectively, we summarize the commonly adopted methodologies in the corresponding analysis and design, then describe important works in the literature in detail. Finally, potential research directions and challenges are discussed to provide an outlook for future works in the area of UAV-PLS in 5G and beyond networks.

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“…To further verify the consistency of proposed model and generation method, it is necessary to compare the generated result with field-measurement data. To the best of our knowledge, so far only the authors in [64] analyzed the ACF based on the measurement data for UAV mmWave communications. For the measurement scenario, the RX is placed on a building roof with the height of 25 m and the UAV performs five round-trip flights with the height of 0∼24 m at the constant velocity of 1 m/s.…”

Section: B Validation Of Proposed Channel Modelmentioning

confidence: 99%

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

Mao,

Zhu,

Song

et al. 2021

Preprint

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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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Multi-Frequency Air-to-Ground Channel Measurements and Analysis for UAV Communication Systems

Cited by 41 publications

References 23 publications

Increasing Reliability on UAV Fading Scenarios

Increasing Reliability on UAV Fading Scenarios

Physical Layer Security for UAV Communications in 5G and Beyond Networks

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

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