Unmanned Aerial Vehicle Channel Research Articles

A Joint Channel Allocation and Power Control Scheme for D2D Communication in UAV‐Based Networks

With the increasing application of unmanned aerial vehicles (UAVs), UAV‐based base stations (BSs) have been widely used. In some situations when there is no ground BSs, such as mountainous areas and isolated islands, or BSs being out of service, like disaster areas, UAV‐based networks may be rapidly deployed. In this paper, we propose a framework for UAV deployment, power control, and channel allocation for device‐to‐device (D2D) users, which is used for the underlying D2D communication in UAV‐based networks. Firstly, the number and location of UAVs are iteratively optimized by the particle swarm optimization‐ (PSO‐) Kmeans algorithm. After UAV deployment, this study maximizes the energy efficiency (EE) of D2D pairs while ensuring the quality of service (QoS). To solve this optimization problem, the adaptive mutation salp swarm algorithm (AMSSA) is proposed, which adopts the population variation strategy, the dynamic leader‐follower numbers, and position update, as well as Q‐learning strategy. Finally, simulation results show that the PSO‐Kmeans algorithm can achieve better communication quality of cellular users (CUEs) with fewer UAVs compared with the PSO algorithm. The AMSSA has excellent global searching ability and local mining ability, which is not only superior to other benchmark schemes but also closer to the optimal performance of D2D pairs in terms of EE.

Optimizing Energy Efficiency for Supporting Near‐Cloud Access Region of UAV‐Based NOMA Networks in IoT Systems

Nonorthogonal multiple access (NOMA) and unmanned aerial vehicle (UAV) are two promising technologies for the wireless fifth generation (5G) networks and beyond. On the one hand, UAVs can be deployed as flying base stations to build line‐of‐sight (LoS) communication links to two ground users (GUs) and to improve the performance of conventional terrestrial cellular networks. On the other hand, NOMA enables the share of an orthogonal resource to multiple users simultaneously, thus improving the spectral efficiency and supporting massive connectivities. This paper presents two protocols, namely, cloud‐based central station‐ (CCS‐) based power‐splitting protocol (PSR) and time‐switching protocol (TSR), for simultaneous wireless information and power transmission (SWIPT) at UAV employed in power domain NOMA‐based multitier heterogeneous cloud radio access network (H‐CRAN) of Internet of Things (IoT) system. The system model with k types of UAVs and two users in which the CCS manages the entire H‐CRAN and operates as a central unit in the cloud is proposed in our work. Closed‐form expressions of throughput and energy efficiency (EE) for UAVs are derived. In particular, the EE is determined for the impacts of power allocation at CCS, various UAV types, and channel environment. The simulation results show that the performance for CCS‐based PSR outperforms that for CCS‐based TSR for the impacts of power allocation at the CCS. On the contrary, the TSR protocol has a higher EE than the PSR in the cases of the impact of various UAV types and channel environment. The analytic results match Monte Carlo simulations.

Height Optimization and Resource Allocation for NOMA Enhanced UAV-Aided Relay Networks

In this paper, we investigate the application of the non-orthogonal multiple access (NOMA) technique into the unmanned aerial vehicle (UAV) aided relay networks. Specifically, we first incorporate the NOMA protocol with the decode-and-forward (DF) relay protocol to enhance the performance of the cell edge users in a macrocell network. Theoretical analysis indicates that the NOMA-DF-relay protocol outperforms the conventional orthogonal multiple access (OMA) based DF-relay protocol in terms of data rate. To fully exploit the advantages of the proposed protocol, we formulate a joint UAV height optimization, channel allocation, and power allocation problem with the objective to maximize the total data rate of the cell edge users under the coverage of the UAV. For solving the formulated problem effectively, we first analyze its property and employ the golden section method to propose a general framework to obtain the optimal height of the UAV. Then, we design a low-complexity iterative algorithm to solve the joint channel-and-power allocation problem based on the matching theory and the Lagrangian dual decomposition technique. Finally, simulation results demonstrate that the NOMA-DF-relay protocol is superior to the OMA-DF-relay protocol even when the system parameters are not optimized, and the proposed algorithms can further significantly improve the network performance in comparison with the other schemes.

Cognition in UAV-Aided 5G and Beyond Communications: A Survey

In recent years, unmanned aerial vehicles (UAVs) have attained significant interest in different applications including aerial surveillance, providing wireless coverage, precision agriculture, power lines & oil rigs monitoring and construction, etc. The UAVs implicit peculiarities, e.g., swift mobility, increase in payload capabilities and airborne time, place it as a potential candidate for many applications in next-generation wireless communications. In this article, a comprehensive study on UAVs challenges, potential applications, and regulations is presented. In particular, fundamental challenges such as UAVs standardization, channel modeling, interference mitigation, collision avoidance and optimal trajectory design using deep reinforcement learning (RL) algorithms, energy harvesting techniques, security, and regulations are explored in light of most recent research development. Moreover, we propose a set of UAVs regulations to ensure its steady social integration and new business opportunities. Finally, various problems and future research directions regarding UAVs are presented.

A Novel 3D UAV Channel Model for A2G Communication Environments Using AoD and AoA Estimation Algorithms

In this article, we propose a three-dimensional (3D) multi-input multi-output (MIMO) channel model for air-to-ground (A2G) communications in unmanned aerial vehicles (UAV) environments, where the UAV transmitter and ground receiver are in motion in the air and on the ground, respectively. A novel angular estimation algorithm is proposed to estimate the real-time azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) based on the non-stationary nature of the channel model. In the model, we investigate the time-varying spatial cross-correlation functions (CCFs) and temporal auto-correlation functions (ACFs) with respect to the different moving directions and velocities of the UAV transmitter and ground receiver. Furthermore, we derive and study the Doppler power spectral densities (PSDs) and power delay profiles (PDPs) of the proposed channel model. Numerical results show that characteristics of the proposed channel model are very close to those of practical measurements, which provide a new and practical approach to evaluate the performance of next generation UAV-MIMO communication systems.

Optimal video communication strategy for intelligent video analysis in unmanned aerial vehicle applications

For Unmanned Aerial Vehicles (UAV), the intelligent video analysis is a key technology in intelligent autonomous control, real-time navigation and surveillance. However, poor UAV wireless links would degrade the quality of video communication, leading to difficulties in video analysis. To meet the challenges of packet-loss and limited bandwidth in adverse UAV channel environments, this paper proposes a parameter optimization mechanism for UAV intelligent video analysis. In the proposed method, an Optimal Strategy Library (OSL) is designed to optimize the parameters for video encoding and forward error correction. Adapted to the packet-loss rate and bandwidth in practical UAV wireless network, the proposed OSL can facilitate the encoding of video sequences and the recovery of degraded videos with optimal performance. Experimental results demonstrate that the proposed solution can keep intelligent video analysis working efficiently with adverse UAV wireless links, and is capable of maximizing the inference accuracy of Multi-Object Tracking (MOT) algorithms in various scenarios.

Unmanned Aerial Vehicle Communications: Path-Loss Modeling and Evaluation

Channel modeling plays an important role in unmanned aerial vehicle (UAV) communication system design and performance evaluation. In this article, we provide a brief review of the existing UAV channel modeling, emphasizing that UAV altitude is a significant factor in characterizing large-scale fading modeling. We then investigate the effect of UAV altitude on the system-level performance of various networks. Simulation results are provided to show the behavior of cellular-connected UAV networks with different channel models and conditions. In addition, we show that antenna design significantly affects system performance. (Table 1 summarizes the abbreviations used in the article.)

Application of hyperbolic partial differential equations in global optimal scheduling of UAV

The global optimal scheduling of UAV(unmanned aerial vehicle)navigation channel is studied. Firstly, a multi-channel optimal scheduling mathematical model based on the HPDEs (hyperbolic partial differential equations) wave combination optimization is proposed. Secondly, the pseudo arc length method is applied to solve the HPDEs, and the form of the spatial transformation of the global pseudo arc length method and its mesh adaptive properties are given. Finally, the results show that the mathematical model is used to achieve better convergence of the UAV's channel scheduling, and the scheduling delay error is lower, which has better reliability and robustness.

A Wideband Non-Stationary Air-to-Air Channel Model for UAV Communications

Unmanned aerial vehicle (UAV) communications have recently drawn great attention. To analyze and optimize the UAV communication systems, accurate and efficient UAV channel models are essential. Most existing UAV channel models are based on the assumption that both transmitter (Tx) and receiver (Rx) have fixed velocity and moving direction. However, in the realistic communication scenarios, the Tx and Rx of UAV may experience changes in both speeds and trajectories. In this paper, a three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) is proposed for air-to-air (A2A) channels in UAV communication scenarios. A 3D Markov mobility model is used to characterize the movements of the UAV in both horizontal and vertical directions. The mobility model enables to adjust the degree of mobility randomness and covers different UAV mobility trajectories, which makes it more realistic and suitable for simulating various scenarios. In addition, some important statistical properties such as time-frequency correlation function and Doppler power spectrum are derived and analyzed. The numerical results show that the impact of vertical movement of the UAV on the time correlation function under constant-speed is larger than that of horizontal movement, however, the above phenomenon is mitigated and the non-stationarity properties of the channel are significantly affected when randomness of the mobility model is introduced. Furthermore, it is found that the movement properties of the UAV only lead to non-stationarity in the time domain. The results are useful for A2A non-stationary channel simulation and UAV communication system evaluation.

Envelope Level Crossing Rate and Average Fade Duration of a Generic 3D Non-Stationary UAV Channel Model

For the unmanned aerial vehicle (UAV) communication scenarios, the time-varying fading characteristics cased by the three-dimensional(3D) scattering environment and 3D trajectory have an important impact on establishing the reliable communication link. In this paper, a generic 3D non-stationary geometry-based stochastic model (GBSM) for UAV channels is developed. Different from the traditional 3D GBSMs, the proposed model takes the 3D arbitrary trajectories of UAV into account. On this basis, two important second-order statistical properties of fading envelope, i.e., the level crossing rate (LCR) and average fading duration (AFD), are investigated and derived in details. The obtained closed-form expressions are explicit functions of flight parameters and compatible with the results in the previous works. Numerical simulations show that the simulated results are in accordance with the theoretical and measurement data under 3D flight scenarios. The proposed model and statistical properties can be applied to the optimal design of channel coding and block interleave schemes for UAV communication systems.

Design and Analysis of UAV-Assisted Relaying With Simultaneous Wireless Information and Power Transfer

We consider a two-phase decode-and-forward (DF) relay network assisted by an unmanned aerial vehicle (UAV), where the UAV performs energy harvesting and information decoding simultaneously with a power splitting (PS) receiver structure. For the network, we optimize the PS and time allocation (TA) factors to minimize the outage probability in transferring the data from a source to a destination suffering from blockages in the direct link. The optimal solution is found in two steps, optimization of the PS factor for given TA and then optimization of the TA factor using the conditionally optimized PS factor given in a closed-form. To reduce the complexity of the optimal method, we next propose a suboptimal method using a fixed TA determined by the transmission rate only and a fixed PS ffactor given by a function of the average power of the UAV-to-destination channel. The outage probability of the proposed method is analyzed in a generalized UAV channel model including path-loss, shadowing, and Nakagami-m fading, which is shown to agree with the simulation results and is close to the optimal performance. In addition, the optimal UAV position is investigated in various channel environments, which reveals that the optimal altitude depends on the deployment scenarios and the optimal ground position is the center-point between the source and destination in general.

Effect of User Mobility and Channel Fading on the Outage Performance of UAV Communications

Many wireless networks operate in a mobile environment with randomly moving user terminals. This letter analytically characterizes the impact of ground user mobility, propagation environment and channel fading on the outage performance of unmanned aerial vehicle (UAV) communications. Closed-form expressions for the outage probability using the random waypoint model for ground user mobility, UAV channel models for different propagation environments and the Nakagami-m model for fading channels are derived. Furthermore, the outage analysis takes into account the effect of co-channel interference by both the stationary and mobile users. Numerical results are presented to demonstrate the interplay between the communication performance and the system parameters.

3D non‐stationary unmanned aerial vehicles' MIMO channel model

The unmanned aerial vehicle (UAV) channel measurements have shown that the propagation environment of UAV channel is non-stationary. In this study, a three-dimensional (3D) non-stationary UAV multiple-input multiple-output (MIMO) channel model is proposed. In order to sufficiently describe the propagation environment of UAV-MIMO channel, the stationary scatterers and moving scatterers are investigated in the proposed model, which are mimicked using 3D cylinder and two-dimensional (2D) disc, respectively. The Doppler shifts associated with moving scatterers are also considered in the proposed model. The movement of UAV, receiver, and moving scatterers results in non-stationarities, and time-variant distances, azimuth angles, and elevation angles are investigated in the proposed model. The statistical properties of the moving scatterers are also investigated in the proposed channel model. In order to validate the proposed model, the spatial correlation is compared with the measurement results, and the numerical results show that the proposed channel model is applicable to describe the UAV-MIMO communication systems.

Sensor-Augmented Neural Adaptive Bitrate Video Streaming on UAVs

Recent advances in unmanned aerial vehicle (UAV) technology have revolutionized a broad class of civil and military applications. However, the designs of wireless technologies that enable real-time streaming of high-definition video between UAVs and ground clients present a conundrum. Most existing adaptive bitrate (ABR) algorithms are not optimized for the air-to-ground links, which usually fluctuate dramatically due to the dynamic flight states of the UAV. In this paper, we present SA-ABR, a new sensor-augmented system that generates ABR video streaming algorithms with the assistance of various kinds of inherent sensor data that are used to pilot UAVs. By incorporating the inherent sensor data with network observations, SA-ABR trains a deep reinforcement learning (DRL) model to extract salient features from the flight state information and automatically learn an ABR algorithm to adapt to the varying UAV channel capacity through the training process. SA-ABR does not rely on any assumptions or models about UAV's flight states or the environment, but instead, it makes decisions by exploiting temporal properties of past throughput through the long short-term memory (LSTM) to adapt itself to a wide range of highly dynamic environments. We have implemented SA-ABR in a commercial UAV and evaluated it in the wild. We compare SA-ABR with a variety of existing state-of-the-art ABR algorithms, and the results show that our system outperforms the best known existing ABR algorithm by 21.4% in terms of the average quality of experience (QoE) reward.

A 3D Non-Stationary Wideband GBSM for Low-Altitude UAV-to-Ground V2V MIMO Channels

Due to the high-mobility of unmanned aerial vehicles (UAVs), actual UAV channel measurements show that low-altitude air-to-ground (A2G) channels in UAV communications illustrate non-stationary properties. This fact motivates us to develop a non-stationary channel model for UAV-to-ground links. In this paper, we propose a three-dimensional (3D) wideband non-stationary A2G vehicle-to-vehicle (V2V) geometry-based stochastic channel model (GBSM). In the proposed model, both UAVs and ground terminals can be moving, which makes the model more general. In order to mimic the non-stationary channel characteristics, parameters like the number of clusters, power, time delays, angels of departure (AoDs), and angles of arrival (AoAs) are all time-variant. The proposed model combining a line-of-sight (LoS) component, a ground reflection component, a cylinder model, and multiple confocal truncated ellipsoid models has the ability to investigate the impact of UAV heights and transceivers' movements on channel characteristics in diverse environments. Statistical properties like temporal autocorrelation function (ACF), spatial cross-correlation function (CCF), Doppler power spectral density (PSD), and stationary interval of A2G channels are derived and analyzed in detail. In addition, derived root mean square delay spread (RMS-DS), temporal ACF and spatial CCF are validated against channel measurement results. Furthermore, by adjusting channel parameters, the proposed GBSM is sufficiently generic and adaptable to model various UAV-to-ground communication scenarios.

Three-Dimensional Non-Stationary Wideband Geometry-Based UAV Channel Model for A2G Communication Environments

In this paper, we propose a novel three-dimensional multiple-input multiple-output channel model to describe the air-to-ground (A2G) communication environments. The model introduces the unmanned aerial vehicle (UAV) transmitter and ground mobile receiver (MR) located at the foci points of the boundary ellipsoid, while different ellipsoids represent the propagation properties for different time delays. In light of this, we are able to investigate the propagation properties of the A2G channel model for different time delays. Furthermore, the time-varying parameters of the azimuth angle of departure, the elevation angle of departure, the azimuth angle of arrival, and elevation angle of arrival are derived to properly describe the channel non-stationarity, which is caused by the motion of the UAV transmitter, cluster, and MR. The impacts of the movement properties of the cluster in both the azimuth and elevation planes are investigated on the channel characteristics, i.e., spatial cross-correlation functions, temporal autocorrelation functions, Doppler power spectrum density, and power delay profiles.

Efficient channel tracking strategy for mmWave UAV communications

Unmanned aerial vehicles (UAV) communications attract ever increasing attentions due to its flexible deployment. The characteristics of the time-varying UAV channel with beam squint effect are explored. Then, a channel tracking method for UAV communications is proposed. Finally, various and numerous simulation results are provided to verify the effectiveness of the proposed method.

A Survey of Channel Modeling for UAV Communications

Unmanned aerial vehicles (UAVs) have attracted great interest in rapid deployment for both civil and military applications. UAV communication has its own distinctive channel characteristics compared to the widely used cellular or satellite systems. Accurate channel characterization is crucial for the performance optimization and design of efficient UAV communication. However, several challenges exist in UAV channel modeling. For example, the propagation characteristics of UAV channels are under explored for spatial and temporal variations in non–stationary channels. Additionally, airframe shadowing has not yet been investigated for small size rotary UAVs. This paper provides an extensive survey of the measurement methods proposed for UAV channel modeling that use low altitude platforms and discusses various channel characterization efforts. We also review from a contemporary perspective of UAV channel modeling approaches, and outline future research challenges in this domain.

UAV Channel Estimation with STBC in MIMO Systems

Abstract The combination of coding with spatial diversity opens up new dimensions in wireless communications with emphasize to Unmanned Aerial Vehicles (UAVs), and can offer effective solutions for wireless communication channels. Application of Alamouti Space-Time Block Codes (STBCs) with diversity with multiple antennas provide improved performance in faded wireless channels. Alamouti transmit diversity scheme, however, relies on the availability of accurate Channel State Information (CSI). This paper proposes a channel estimation method based on Kalman filter with STBC codes and multiple antenna systems, namely Multiple-Input Single-Output (MISO) and Multiple-Input Multiple-Output (MIMO) systems. Simulations have been done in time-varying Rayleigh faded channels for BPSK and QPSK. The proposed technique seems to obtain an error performance closer to that of a known channel information case in severely faded channel considerations.