mmWave Unmanned Aerial Vehicles Research Articles

Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension.

In the era of Industry 5.0, advanced technologies like artificial intelligence (AI), robotics, big data, and the Internet of Things (IoT) offer promising avenues for economic growth and solutions to societal challenges. Digital twin technology is important for real-time three-dimensional space reproduction in this transition, and unmanned aerial vehicles (UAVs) can support it. While recent studies have explored the potential applications of UAVs in nonterrestrial networks (NTNs), bandwidth limitations have restricted their utility. This paper addresses these constraints by integrating millimeter wave (mmWave) technology into UAV networks for high-definition video transmission. Specifically, we focus on coordinating intelligent reflective surfaces (IRSs) and UAV networks to extend coverage while maintaining virtual line-of-sight (LoS) conditions essential for mmWave communication. We present a novel approach for integrating IRS into Beyond 5G/6G networks to enhance high-speed communication coverage. Our proposed IRS selection method ensures optimal communication paths between UAVs and user equipment (UE). We perform numerical analysis in a realistically modeled 3D urban environment to validate our approach. Our results demonstrate significant improvements in the received SNR for multiple UEs upon the introduction of IRSs, and they confirm the feasibility of coverage extension in mmWave UAV networks.

Computer Vision-Aided mmWave UAV Communication Systems

Unmanned aerial vehicle (UAV) communication systems usually operate in harsh scenarios, which require accurate information about the topology and wireless channel to achieve the desired transmission performance. Therefore, when millimeter wave (mmWave) communication with its intrinsic line-of-sight (LoS) condition is adopted, accurate target localization is essential to determine the spatial relationship between the UAV and the grounded receivers (Rxs). In this paper, a computer-vision (CV)-aided jointly optimization scheme of flight trajectory and power allocation is designed for mmWave UAV communication systems by utilizing the visual information captured via cameras equipped at the UAV. Compared with traditional schemes, the implementation cost and overhead can be greatly saved as no radio frequency transmissions are required in the proposed localization scheme. In addition, the transmit power at the UAV is jointly optimized with its flight trajectory in two different cases. Finally, simulation results are presented to demonstrate the efficiency of the proposed schemes.

Intelligent flying-beamformer for hybrid mmWave systems: A deep reinforcement learning approach

Millimeter wave (mmWave) enabled unmanned aerial vehicle (UAV) communications with excellent flexibility and high data transmission capabilities are widely regarded as an essential element of the next generation non-terrestrial networks. In this paper, we investigate the 3D trajectory design, beamwidth and power allocation problems for mmWave UAV communication systems. We first formulate a non-convex optimization problem to maximize the total normalized spectral efficiency (NSE) of all ground terminals (GTs). Then, we propose a deep reinforcement learning (DRL) based framework, termed as intelligent flying-beamformer, to solve the formulated non-convex optimization, which contains of two sequential phases: 3D UAV trajectory design, and joint optimization of beamwidth and power allocation. In particular, the off-policy deep deterministic policy gradient (DDPG) and the on-policy proximal policy optimization (PPO) algorithms are used to process the complex data in the two phases, respectively. Simulation results verify the effectiveness of the proposed intelligent flying-beamformer in improving system capacity. Specifically, numerical results show that the optimization of the beamwidth can indeed significantly improve the spectral efficiency of mmWave UAV network.

Robust Channel Estimation Scheme for Multi-UAV MmWave MIMO Communication with Jittering

In unmanned aerial vehicle (UAV)-assisted millimeter-wave (mmWave) communications, the communication performance is significantly degraded by UAV jitter. We formulate a UAV-assisted mmWave channel model with hybrid beamforming for the impacts of UAV jitter. Then, we derive the distribution of angle of arrivals/departures (AOAs/AODs) with random fluctuation of the UAV attitude angle. We develop an iterative reweight-based robust scheme as the super-resolution AOAs/AODs estimation method. Specifically, we introduce the partially adaptive momentum (Padam) estimation method to optimize the objective function of the jittering UAV mmWave massive multi-input multioutput (MIMO) system. Finally, compared with existing channel estimation schemes, the proposed UAV mmWave channel estimation method can achieve robust super-resolution performance in AOAs/AODs and path gains estimation with numerical results. Therefore, the proposed channel estimation scheme is very suitable for UAV mmWave massive MIMO communications with jittering.

Deployment and Robust Hybrid Beamforming for UAV MmWave Communications

We deploy an unmanned aerial vehicle (UAV) equipped with a large-scale uniform planar array (UPA) to serve multiple ground users in millimeter-wave band. Particularly, the practical UAV jitter is carefully considered, which may affect the beam gains and impact the communication quality. To provide a stable service, we first model the attitude change of the UPA caused by UAV jitter. Then, an optimization problem is formulated to maximize the minimum achievable rate of the users by optimizing the position and robust hybrid beamforming of the UAV. To solve the non-convex problem with highly coupled variables, a two-stage optimization strategy is developed. The first stage aims to decouple the beamforming from the original problem and design the UAV deployment under the assumption of an ideal beam pattern. The second stage aims to design robust hybrid beamforming with the obtained UAV position. Specifically, we first design analog beamforming for wide beams to cover the potential jitter angle range for each user via a chirp sequence-inspired method. Then, with equivalent channel estimation, we design digital beamforming by combining zero-forcing and water-filling power allocation algorithms. Extensive simulation results show the performance superiority of the proposed solution compared to the benchmark algorithms.

A Non-Stationary Model With Time-Space Consistency for 6G Massive MIMO mmWave UAV Channels

In this paper, a novel unmanned aerial vehicle (UAV) space-time-frequency (S-T-F) non-stationary channel model with time-space consistency for sixth generation (6G) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) wireless communication systems is proposed. In the proposed model, the line-of-sight (LoS) transmission and non-LoS (NLoS) transmission through ground reflection, single-clusters, and twin-clusters are modeled. Meanwhile, the three-dimensional (3D) continuously arbitrary trajectory and the self-rotation of UAV are imitated. To capture the time-space consistency and S-T-F non-stationarity simultaneously, a new UAV-related non-stationary modeling algorithm that integrates the visibility region (VR), frequency-dependent path gain, and survival probability is developed for the first time. In this algorithm, the UAV-related parameters are considered, including UAV’s height, 3D moving velocity, and self-rotation angles. In the proposed model, the calculation of channel impulse response (CIR) is developed, which considers the cluster density index influenced by communication scenarios, frequency, UAV’s height, and the distance between transceivers. Some important channel statistical properties, such as S-T-F correlation function (STF-CF), Doppler power spectral density (DPSD), and stationary interval, are derived. Finally, simulation results match well with ray-tracing-based results, which verifies the utility of the proposed model.

Learning-Based Beam Alignment for Uplink mmWave UAVs

Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a deep Q-Network(DQN)-based framework for uplink UAV-BS beam alignment where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information and maximize the beamforming gain upon every communication request from UAV inside the multi-location environment. We compare the proposed framework against multi-armed bandit (MAB)-based and exhaustive approaches, respectively and then analyse its training performance over different coverage area requirements, antenna configurations and channel conditions. Our results show that the proposed framework converge faster than the MAB-based approach and comparable to traditional exhaustive approach in an online manner under real-time conditions. Moreover, this approach can be further enhanced to predict the optimal beams for unvisited UAV locations inside the coverage using correlation from neighbouring grid locations.

A Double-Beam Soft Handover Scheme and Its Performance Analysis for mmWave UAV Communications in Windy Scenarios

Unmanned aerial vehicles (UAVs) acting as users to access the cellular network over the millimeter wave (mmWave) spectrum form a promising solution to guarantee high data rate transmission. Due to the narrow beams in the mmWave systems and the high mobility of the UAVs, a key challenge of mmWave UAV communications is to design efficient and seamless handover schemes, especially in windy scenarios. In this paper, we first investigate the signaling interaction procedure of the hard handover strategy in 3GPP 5G new radio (NR) standard. For providing better connectivity and lower outage probability, a soft handover scheme named double-beam soft handover (DB-SH) is designed for the mmWave UAV system. Considering the dynamic wind effects, we formulate a random walk (RW) model for quantifying the angle offsets of the beams at the UAVs, as the orientations of the UAVs suffer from the fluctuations over time due to the dynamic wind load. Considering the RW model and the beam training, we further analyze the outage probabilities of the 3GPP 5G NR hard handover scheme and the DB-SH scheme for the mmWave UAV communication systems. Finally, our simulation results demonstrate the accuracy of the presented theoretical analysis which provides insights for mmWave UAV system designs.

Distribution of Multi MmWave UAV Mounted RIS Using Budget Constraint Multi-Player MAB

Millimeter wave (mmWave), reconfigurable intelligent surface (RIS), and unmanned aerial vehicles (UAVs) are considered vital technologies of future six-generation (6G) communication networks. In this paper, various UAV mounted RIS are distributed to support mmWave coverage over several hotspots where numerous users exist in harsh blockage environment. UAVs should be spread among the hotspots to maximize their average achievable data rates while minimizing their hovering and flying energy consumptions. To efficiently address this non-polynomial time (NP) problem, it will be formulated as a centralized budget constraint multi-player multi-armed bandit (BCMP-MAB) game. In this formulation, UAVs will act as the players, the hotspots as the arms, and the achievable sum rates of the hotspots as the profit of the MAB game. This formulated MAB problem is different from the traditional one due to the added constraints of the limited budget of UAVs batteries as well as collision avoidance among UAVs, i.e., a hotspot should be covered by only one UAV at a time. Numerical analysis of different scenarios confirm the superior performance of the proposed BCMP-MAB algorithm over other benchmark schemes in terms of average sum rate and energy efficiency with comparable computational complexity and convergence rate.

Effects of Vertical Fluctuations on Air-to-Ground mmWave UAV Communications

Unmanned aerial vehicle (UAV)-based millimeter wave (mmWave) communications have unique mobility and data rate advantages. However, the position of the UAV may fluctuate randomly in the horizontal and vertical directions due to wind, system stability error, or attitude control system fault, which may affect the mmWave channel blockage characteristic and system quality of service (QoS) performance. In this letter, we study the impact of UAV fluctuations in the vertical direction on the blockage and QoS of air-to-ground mmWave UAV communications for the first time. By deriving the closed-form expressions of blockage probability, we find that although the blockage probability is proportional to the variance of the vertical fluctuation, the vertical fluctuation has little effect on the blockage probability due to the small weight of the variance. In contrast, vertical fluctuations significantly impact the reliable service probability. The stronger the vertical fluctuation is, the smaller the reliable service probability and the worse the QoS. Finally, Monte Carlo simulations verify the above conclusions.

A Non-Stationary 6G UAV Channel Model With 3D Continuously Arbitrary Trajectory and Self-Rotation

In this paper, based on the geometric stochastic modeling method, a space-time-frequency (S-T-F) non-stationary model with three-dimensional (3D) continuously arbitrary trajectory and self-rotation is proposed for sixth generation (6G) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) unmanned aerial vehicle (UAV) channels. It is the first 6G massive MIMO mmWave UAV channel model that considers the 3D continuously arbitrary trajectory of UAV in practice and models S-T-F non-stationarity of 6G UAV channels. In the proposed model, the calculation of channel impulse response (CIR) is developed, which considers the 3D time-varying accelerations and self-rotations of transceivers and clusters. To further model the S-T-F non-stationarity of UAV channels, a novel UAV-related birth-death (BD) algorithm based on correlated clusters is developed. In the developed algorithm, the impact of typical UAV-related parameters, e.g., the UAV’s moving direction, altitude, and time-varying velocity, on the setting of correlated clusters and the BD process is sufficiently considered. Important channel statistical properties are derived and investigated. Some numerical results and interesting observations are given, which can provide some assistance for the design of 6G massive MIMO mmWave UAV communication systems. Finally, the utility of the proposed model is verified by the close agreement between simulation results and ray-tracing-based results.

A 3-D Space-Time-Frequency Non-Stationary Model for Low-Altitude UAV mmWave and Massive MIMO Aerial Fading Channels

In this article, a 3-D geometry-based stochastic model (GBSM) is proposed for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) unmanned aerial vehicle (UAV) channels. The proposed model is the first mmWave massive MIMO UAV two-cylinder GBSM that enables to jointly model the channel space-time-frequency non-stationarity by a novel variable parameter-based method. In this novel method, key parameters of UAV channels are assumed to vary in space, time, and frequency domains, and the effect of the unique UAV-related parameters is further taken into account, such as the UAV’s altitude, velocity, and moving directions. Based on the proposed model, some statistical properties are derived, including the time-variant transfer function (TVTF), space-time-frequency correlation function (STF-CF), Doppler power spectral density (PSD), and the standard deviation of Doppler frequency on antenna arrays. Simulation results show that the channel non-stationarity in space, time, and frequency domains can be captured, and the aforementioned UAV channel-related parameters have a great impact on channel statistics. Finally, the utility of the proposed model is validated by the excellent agreement between simulation results and measurements.

Modeling and Analysis of mmWave UAV Swarm Networks: A Stochastic Geometry Approach

In order to evaluate the impacts of swarm distribution characteristics of unmanned aerial vehicles (UAVs) and the complexity of millimeter wave (mmWave) propagations on the coverage performance of UAV networks, this paper develops a stochastic geometry-based approach for the modeling and analysis of single- and multi-swarm UAV networks with mmWave communications. We first utilize binomial point processes (BPPs) to model the locations of UAVs in a finite area as a single-swarm UAV network and allow for arbitrary user locations. Then, we define multi-BPPs and extend our model to multi-swarm UAV networks. We consider an open-access strategy, where users access the closest UAV in all swarms, and derive the distance distributions of serving and interfering UAVs. Moreover, a three-dimensional (3D) blockage model and a 3D sectorized antenna model are introduced to completely depict the characteristics of air-to-ground channel in mmWave band. Based on the distance distributions and the Laplace transform of interference, coverage probabilities in considered networks are derived. Our results reveal that, there exists optimal height and density of UAV swarms that maximize the coverage probability. When densely deployed, the interference from inter-swarm cannot be negligible in the multi-swarm UAV network.

A Mixed-Bouncing Based Non-Stationary Model for 6G Massive MIMO mmWave UAV Channels

This paper proposes a novel three-dimensional (3D) mixed-bouncing based unmanned aerial vehicle (UAV) channel model with space-time-frequency (S-T-F) non-stationarity for sixth generation (6G) massive multiple-input-multiple-output (MIMO) millimeter wave (mmWave) wireless communication systems. In the proposed mixed-bouncing based model, the line-of-sight (LoS) transmission, single-bouncing transmission, and multi-bouncing transmission are simultaneously modeled. A new parameter, i.e., cluster density index, is defined and developed. The influence of communication scenarios, frequency, UAV’s height, and distance between transceivers on cluster density index is also analyzed. To capture the S-T-F non-stationarity, a mixed-bouncing based S-T-F non-stationary algorithm is developed based on the birth-death (BD) process and frequency-dependent factor for the first time. In the developed algorithm, the non-stationarity is captured from the perspectives of both single-clusters and twin-clusters. Meanwhile, the impact of UAV-related parameters, such as the UAV’s height and 3D moving velocity, on the non-stationary modeling is considered. Based on the simulation results, the impact of cluster density index on the important channel statistical properties, such as S-T-F correlation function (STF-CF) and Doppler power spectral density (PSD), is explored. Finally, the simulation results match well with the measurement and ray-tracing (RT)-based results, validating the accuracy of the proposed model.

A Non-Stationary 3D Model for 6G Massive MIMO mmWave UAV Channels

This paper proposes a non-stationary three-dimensional (3D) irregular-shaped geometry-based stochastic model (IS-GBSM) for fifth generation (5G) and beyond massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) unmanned aerial vehicle (UAV) channels. This is the first sixth generation (6G) massive MIMO mmWave UAV IS-GBSM that can model the UAV channel space-time non-stationarity, and can describe the impact of some unique UAV-related parameters, e.g., the UAV’s moving direction, height, and speed, on channel statistical properties. To better represent the space-time non-stationarity in UAV scenarios, a novel UAV-related space-time cluster evolution algorithm is developed. The developed algorithm considers the characteristics of UAV communications on the modeling of space-time non-stationarity. Based on the proposed model, some channel statistical properties are derived and thoroughly investigated, including the space-time-frequency correlation function, Doppler power spectral density, envelope level crossing rate, and average fade duration. Some numerical results and interesting observations are given, and the impact of UAV-related parameters on channel statistical properties is explored, which can provide assistance for the design of 6G massive MIMO mmWave UAV communication systems. Finally, the applicability of the proposed model is verified by the close agreement between simulation results and measurement.

Joint Doppler shift and channel estimation for UAV mmWave system with massive ULA

A joint Doppler shift and channel estimation method for the millimeter-wave communication system of an unmanned aerial vehicle (UAV) equipped with a large-scale uniform linear antenna (ULA) array has been proposed. Since Doppler shift induces intercarrier interference, the parameters of the channel paths have been decomposed into the Doppler shift and the channel information. In order to obtain the Doppler shift, a new estimation algorithm based on a combination of discrete Fourier transform and phase rotation has been proposed, which can determine the appropriate number of antennas. In addition to estimating the channel information, a low-complexity joint Doppler shift and channel estimation method has been designed that can quickly obtain accurate estimates. Furthermore, the achievable sum rate, the theoretical bounds of the mean squared errors, and the Cramér-Rao lower bounds of the estimation method have been derived. The analysis and simulation results prove that the performance of the proposed approach is close to the theoretical inference.

Regression-based beam training for UAV mmWave communications

For unmanned aerial vehicle (UAV) millimeter-wave (mmWave) communication systems, efficient and accurate beam training is urgently required to overcome beam misalignment. By taking into account the mmWave propagation environment, a three-dimensional (3D) intelligent beam training strategy that leverages the polynomial regression (PR) model and optimized beam patterns is proposed in this paper. We treat mmWave beam selection as a PR problem. By using machine learning (ML), the regression function is determined. The training dataset applied in the ML method consists of measured power and estimated angles and is obtained by carefully designed beam patterns. Furthermore, a noise suppression method involving the use of a denoising autoencoder (DAE) is developed to overcome the noise sensitivity of the proposed regression model. The numerical simulation results demonstrate that our proposed beam training strategy is capable of obtaining the same precision as an exhaustive search with a shorter time.

Outage Analysis for Cooperative mmWave UAV Communications With Beam Training Overhead

In this letter, we investigate the tradeoff between reliability and beam training overhead in cooperative millimeter wave (mmWave) unmanned aerial vehicle (UAV) communications. In particular, we analyze how outage probability varies with the number of neighboring UAVs being probed as potential relays for relay selection. Unlike existing work, the limited number of UAV beams as well as UAV orientations are explicitly considered in our analysis. We derive the outage probability in closed form and verify our analysis via simulations. The results facilitate us to understand the fundamental limits of cooperative mmWave UAV communications.

A Non-Stationary Geometry-Based MIMO Channel Model for Millimeter-Wave UAV Networks

Unmanned aerial vehicle (UAV) communications are expected to play a major role in future space-air-ground integrated networks (SAGINs). In this paper, a geometric three-dimensional (3D) non-stationary channel model operating at millimeter-wave (mmWave) band is proposed for wideband UAV multiple-input multiple-output (MIMO) communications based on a multiple-layer cylinder reference model, where both stationary and moving clusters around transmitter (Tx) and receiver (Rx) are considered. Unlike the existing UAV-based GBSMs, the proposed model considers both local and far clusters in the propagation environments. On this basis, a continuous-time Markov model with two states is used to model the dynamic properties of clusters (i.e., clusters appear/disappear with time), and the closed-form expressions of the survival probabilities of clusters are derived. Furthermore, we derive and investigate some significant statistical properties, including space-time-frequency correlation function, quasi-stationary interval, and Doppler power spectrum. Numerical results show that the local mobile cluster (LMC) component leads to higher time correlation compared with the local stationary cluster (LSC) component. In addition, it is found that the LMC component leads to larger quasi-stationary interval compared with the LSC component. Finally, it is found that the motion of transceivers and clusters, and the changes of carrier frequency introduce significant fluctuations in Doppler power spectrum. These observations and conclusions can be considered as a guidance for mmWave UAV MIMO system design.

Data-Driven Beam Management With Angular Domain Information for mmWave UAV Networks

Unmanned aerial vehicles (UAVs) have extensive civilian and military applications, but establishing a UAV network providing high data rate communications with low delay is a challenge. Millimeter wave (mmWave), with its high bandwidth nature, can be adopted in the UAV network to achieve high speed data transfer. However, it is difficult to establish and maintain the mmWave communication links due to the mobility of UAVs. In this paper, a beam management scheme utilizing angular domain information (ADI) is proposed to rapidly establish and reliably maintain the communication links for the mmWave UAV network. Firstly, Gaussian process machine learning (GPML)-enabled position prediction is proposed to facilitate coarse-ADI acquisition through the proposed UAV clustering algorithm. Then, with the proposed confined-ADI acquisition which removes the redundancy in the coarse-ADI acquisition, fast beam tracking with respectively the single-beam pattern and the multi-beam pattern is achieved. Finally, a data-driven beam pattern selection scheme is proposed for improving the spectrum efficiency. Simulation results verify the outstanding performance of the proposed beam management for mmWave UAV networks.