Lightweight UAV-based Measurement System for Air-to-Ground Channels at 28 GHz

Semkin, Vasilii; Kang, Seok Kwon; Haarla, Jaakko; Xia, William; Huhtinen, Ismo; Geraci, Giovanni; Lozano, Angel; Loianno, Giuseppe; Mezzavilla, Marco; Rangan, Sundeep

doi:10.1109/pimrc50174.2021.9569561

Cited by 18 publications

(9 citation statements)

References 21 publications

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“…Following [6], the authors in [25] studied the Doppler effect at millimeter-wave (mmWave) frequencies for an air-to-ground channel under random UAV wobbling. Apart from analytical results, measurement campaigns have also reported the impact of wobbling as an important source of error in establishing a strong connection in aerial wireless networks [26], [27]. In particular, in [26], the authors used a channel sounder to investigate an air-to-ground wireless link, from which they observed that the variations of the received power from a completely static UAV are much less than that of a hovering UAV.…”

Section: A Related Workmentioning

confidence: 99%

Fundamentals of Wobbling and Hardware Impairments-Aware Air-to-Ground Channel Model

Banagar¹,

Dhillon²

2022

Preprint

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In this paper, we develop an impairments-aware air-to-ground unified channel model that incorporates the effect of both wobbling and hardware impairments, where the former is caused by random physical fluctuations of unmanned aerial vehicles (UAVs), and the latter by intrinsic radio frequency (RF) nonidealities at both the transmitter and receiver, such as phase noise, in-phase/quadrature (I/Q) imbalance, and power amplifier (PA) nonlinearity. The impact of UAV wobbling is modeled by two stochastic processes, i.e., the canonical Wiener process and the more realistic sinusoidal process. On the other hand, the aggregate impact of all hardware impairments is modeled as two multiplicative and additive distortion noise processes, which is a well-accepted model. For the sake of generality, we consider both wide-sense stationary (WSS) and nonstationary processes for the distortion noises. We then rigorously characterize the autocorrelation function (ACF) of the wireless channel, using which we provide a comprehensive analysis of four key channel-related metrics: (i) power delay profile (PDP), (ii) coherence time, (iii) coherence bandwidth, and (iv) power spectral density (PSD) of the distortion-plusnoise process. Furthermore, we evaluate these metrics with reasonable UAV wobbling and hardware impairment models to obtain useful insights. Quite noticeably, we demonstrate that even for small UAV wobbling, the coherence time severely degrades at high frequencies, which renders air-to-ground channel estimation very difficult at these frequencies. To the best of our understanding, this is the first work that characterizes the joint impact of UAV wobbling and hardware impairments on the air-to-ground wireless channel.

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Section: A Related Workmentioning

confidence: 99%

Fundamentals of Wobbling and Hardware Impairments-Aware Air-to-Ground Channel Model

Banagar¹,

Dhillon²

2022

Preprint

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“…For instance, current 3GPP standard-defined aerial channel models are calibrated only for sub-6 GHz frequencies [3]. Attempts to create the first measurements-based mmWave aerial channel model [148]- [150] focus exclusively on LoS links and are still missing a statistical description of the multipath channel, which is essential to enable wireless coverage in a multitude of scenarios, as discussed in Sec. IV.…”

Section: A Ai For Aerial Channel Modelingmentioning

confidence: 99%

“…Enter UAVs, with new challenges in the domain of channel characterization such as the effects of altitude and trajectories, the uncertainties associated with wind [237], the orientation and wobbling of the arrays [238], [239], or possible signal blockages by the body of the UAV [240]. The power consumption aspect also becomes exacerbated, and additional limitations might be imposed because of size and weight considerations.…”

Section: Uav Communication At Thz Frequenciesmentioning

confidence: 99%

What Will the Future of UAV Cellular Communications Be? A Flight from 5G to 6G

Geraci¹,

García‐Rodríguez²,

Azari³

et al. 2021

Preprint

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What will the future of UAV cellular communications be? In this tutorial article, we address such a compelling yet difficult question by embarking on a journey from 5G to 6G and sharing a large number of realistic case studies supported by original results. We start by overviewing the status quo on UAV communications from an industrial standpoint, providing fresh updates from the 3GPP and detailing new 5G NR features in support of aerial devices. We then show the potential and the limitations of such features. In particular, we demonstrate how sub-6 GHz massive MIMO can successfully tackle cell selection and interference challenges, we showcase encouraging mmWave coverage evaluations in both urban and suburban/rural settings, and we examine the peculiarities of direct device-todevice communications in the sky. Moving on, we sneak a peek at next-generation UAV communications, listing some of the use cases envisioned for the 2030s. We identify the most promising 6G enablers for UAV communication, those expected to take the performance and reliability to the next level. For each of these disruptive new paradigms (non-terrestrial networks, cellfree architectures, artificial intelligence, reconfigurable intelligent surfaces, and THz communications), we gauge the prospective benefits for UAVs and discuss the main technological hurdles that stand in the way. All along, we distil our numerous findings into essential takeaways, and we identify key open problems worthy of further study.

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“…The channel characteristics, i.e., path loss, shadowing, and smallscale fading were also extracted and analyzed. Recently, the UAV mmWave channel modeling has attracted more and more interests, which mainly includes the field-measurement based model [18]- [20], the ray-tracing (RT) based model [21]- [24] and the geometry-based stochastic model (GBSM) [25]- [27]. For example, air-to-ground channel sounding systems were implemented based on the Octocopter, hexacopter and DJI UAV in [18]- [20], and measurement campaigns were performed at 16 GHz, 60Hz, and 28 GHz, respectively.…”

mentioning

confidence: 99%

“…Recently, the UAV mmWave channel modeling has attracted more and more interests, which mainly includes the field-measurement based model [18]- [20], the ray-tracing (RT) based model [21]- [24] and the geometry-based stochastic model (GBSM) [25]- [27]. For example, air-to-ground channel sounding systems were implemented based on the Octocopter, hexacopter and DJI UAV in [18]- [20], and measurement campaigns were performed at 16 GHz, 60Hz, and 28 GHz, respectively. Field-measurement based channel modeling is an accurate method to guarantee realistic channel characteristics for the specific scenario, but the hardware implementation is not only expensive but also extremely complicated due to the limitation of volume and payload weight for UAVs.…”

mentioning

confidence: 99%

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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Lightweight UAV-based Measurement System for Air-to-Ground Channels at 28 GHz

Cited by 18 publications

References 21 publications

Fundamentals of Wobbling and Hardware Impairments-Aware Air-to-Ground Channel Model

Fundamentals of Wobbling and Hardware Impairments-Aware Air-to-Ground Channel Model

What Will the Future of UAV Cellular Communications Be? A Flight from 5G to 6G

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

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