3D Placement and Orientation of mmWave-Based UAVs for Guaranteed LoS Coverage

Sabzehali, Javad; Shah, Vijay K.; Dhillon, Harpreet S.; Reed, Jeffrey H.

doi:10.1109/lwc.2021.3076463

Cited by 38 publications

(19 citation statements)

References 13 publications

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“…Note that UAV wobbling does not play any role in determining the PDP. Further, from (17), we observe that the PDP will be proportional to the pdfs of the propagation delays of different MPCs [52, p. 348]. Using Theorem 1, we can also determine the average and root-mean-square (rms) delay spreads for nonstationary and WSS hardware impairments, respectively, as…”

Section: Metrics: General Resultsmentioning

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: Metrics: General Resultsmentioning

confidence: 99%

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

Banagar¹,

Dhillon²

2022

Preprint

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“…Under ideal channel conditions, it can be assumed that there is neither intra-cell interference (i.e., UAV and it's associated IoT devices) nor inter-cell interference (i.e., UAV to UAV). This is realistically possible by employing non-overlapping orthogonal channels for both access links [33] and backhaul links [34]. This results in the channels being lossless so that no packet drops occur.…”

Section: A Network Modelmentioning

confidence: 99%

Learning based Age of Information Minimization in UAV-relayed IoT Networks

Choudhury¹,

Karmakar²,

Shah³

et al. 2022

Preprint

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Unmanned Aerial Vehicles (UAVs) are used as aerial base-stations to relay time-sensitive packets from IoT devices to the nearby terrestrial base-station (TBS). Scheduling of packets in such UAV-relayed IoT-networks to ensure fresh (or up-todate) IoT devices' packets at the TBS is a challenging problem as it involves two simultaneous steps of (i) sampling of packets generated at IoT devices by the UAVs [hop-1] and (ii) updating of sampled packets from UAVs to the TBS [hop-2]. To address this, we propose Age-of-Information (AoI) scheduling algorithms for two-hop UAV-relayed IoT-networks. First, we propose a low-complexity AoI scheduler, termed, MAF-MAD that employs Maximum AoI First (MAF) policy for sampling of IoT devices at UAV (hop-1) and Maximum AoI Difference (MAD) policy for updating sampled packets from UAV to the TBS (hop-2). We prove that MAF-MAD is the optimal AoI scheduler under ideal conditions (lossless wireless channels and generateat-will traffic-generation at IoT devices). On the contrary, for general conditions (lossy channel conditions and varying periodic traffic-generation at IoT devices), a deep reinforcement learning algorithm, namely, Proximal Policy Optimization (PPO)-based scheduler is proposed. Simulation results show that the proposed PPO-based scheduler outperforms other schedulers like MAF-MAD, MAF, and round-robin in all considered general scenarios.

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“…In [19] the authors use stochastic geometry tools in order to analyze the LoS probability, interference and achievable capacity in order to ind the optimal altitude of the DBSs. The work in [20] aims to provide LoS coverage to all ground users by adjusting the orientation of the DBS antenna array. This work does not address the problem of MIMO beamforming.…”

Section: Related Workmentioning

confidence: 99%

Optimal beamforming and deployment of millimeter-wave drone base stations

Girici¹,

Yurekli²

2022

ITU J-FET

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Due to their flexible deployment and line-of-sight channel conditions, using drones as base stations is a promising technology. Recent developments in Massive MIMO transmission with millimeter-wave beamforming also enables high data rates and enables simultaneous transmission to multiple ground users. In this work we consider the problem of deployment (i.e. positioning) of drone base stations for maximizing proportional fairness, along with analog MIMO beamforming that maximizes the Signal to Interference plus Noise Ratio (SINR). Simulation results reveal that careful K-means clustering of ground users and altitude adjustment performs close to a Particle Swarm Optimization (PSO)-based solution. Discrete Fourier Transform (DFT) codebook-based low-complexity beamforming also provides a promising performance, when compared to the Semi-Definite Programming (SDP)-based solution, as the number of antennas is increased.

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3D Placement and Orientation of mmWave-Based UAVs for Guaranteed LoS Coverage

Cited by 38 publications

References 13 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

Learning based Age of Information Minimization in UAV-relayed IoT Networks

Optimal beamforming and deployment of millimeter-wave drone base stations

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