Air-ground Channel Research Articles

DME/TACAN Multipath Impact on GNSS L5/E5a Airborne Receivers Part II: Air–Ground Channel Model and Application

DME/TACAN Multipath Impact on GNSS L5/E5a Airborne Receivers Part II: Air–Ground Channel Model and Application

UAV-Enabled Integrated Sensing and Communication: Opportunities and Challenges

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) has attracted growing research interests in the context of sixth-generation (6G) wireless networks, in which UAVs will be exploited as aerial wireless platforms to provide better coverage and enhanced sensing and communication (S&C) services. However, due to the size, weight, and power (SWAP) constraints of UAVs, their controllable mobility, and the line-of-sight (LoS) air-ground channels, UAVenabled ISAC introduces new opportunities and challenges. This article provides an overview of UAV-enabled ISAC and proposes various solutions for optimizing the S&C performance. In particular, we first introduce UAV-enabled joint S&C and discuss UAV motion control, wireless resource allocation, and interference management for ISAC systems employing single and multiple UAVs. Then, we present two application scenarios for exploiting the synergy between S&C, namely sensing-assisted UAV communication and communication-assisted UAV sensing. Finally, we highlight several interesting research directions to guide and motivate future work.

UAV-Enabled Secure Communications via Collaborative Beamforming With Imperfect Eavesdropper Information

Unmanned aerial vehicles (UAVs) are playing a pivotal role in wireless networks due to their high mobility and on-demand deployment advantages. However, the UAV-enabled communications are susceptible to be wiretapped by eavesdroppers due to the strong line-of-sight (LoS) dominated air-ground channel. In this paper, we consider a UAV-enabled secure communication scenario, in which a group of UAVs form a UAV-enabled virtual antenna array (UVAA) to transmit information towards the remote base stations (BSs) via collaborative beamforming (CB), while multiple known and unknown eavesdroppers aiming to wiretap the information. Specifically, a secure communication multi-objective optimization problem (SCMOP) is formulated to achieve the maximization of the worst-case secrecy rate, the minimization of the maximum sidelobe level (SLL) as well as the minimization of the flight energy consumption of UAVs by obtaining optimal locations and excitation current weights concerning the UAVs as well as determining an optimal receiver BS that can achieve superior communication performance. To solve the formulated SCMOP which is demonstrated to be non-convex and NP-hard, an improved multi-objective salp swarm algorithm (IMSSA) with several specific operating factors is proposed. Simulations results demonstrate that the proposed IMSSA can deal with the formulated SCMOP effectively and outperforms other benchmark strategies. Moreover, the multi-hop relay is introduced to verify the reasonability of the UVAA system, and two benchmark schemes of the formulated SCMOP are introduced to demonstrate the necessity of the formulated SCMOP. In addition, the performance of the UVAA system under certain unexpected circumstances is estimated. Finally, experimental implementation is conducted by using a Raspberry Pi and the results demonstrate the practicality of the proposed CB-based secure communication approach in real-world scenarios.

Outage Analysis of UAV-Aided Networks With Underlaid Ambient Backscatter Communications

Ambient backscatter communication is an energy efficient technique for massive Internet of Things (IoT). Combining with flexibly deployed unmanned aerial vehicles (UAVs), the UAV-aided ambient backscatter communication can establish wireless links for isolated IoT nodes efficiently. In this paper, we investigate the outage performance of the UAV-aided air-ground network with underlaid ambient backscatter communications, where the emitted signals from the air-ground link are leveraged as radio frequency (RF) carrier for ambient backscattering. The air-ground channel is modeled as a probabilistic line-of-sight (LoS) channel with Nakagami- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> fading. Then, the ground communication is modeled as a non-line-of-sight (NLoS) channel with Rayleigh fading. For the downlink, we derive the expressions of the outage probabilities of the backscatter link and the air-ground link. In addition, the asymptotic cases of infinite transmit power and infinite fading parameter are analyzed. For the uplink, the outage probabilities of the backscatter link and air-ground are analyzed, with the cases of infinite transmit power and fading parameter discussed. Simulation results show that the analytical results match well with the Monte Carlo results, which verifies the effectiveness of the proposed scheme.

UAV-assisted NOMA secure communications: joint transmit power and trajectory optimization

With the inherent advantages of exceptional maneuverability, flexible deployment options and cost-effectiveness, unmanned aerial vehicles (UAVs) present themselves as a viable solution for providing aerial communication services to Internet of Things devices in high-traffic or remote areas. Nevertheless, the openness of the air–ground channel poses significant security challenges to UAV-based wireless systems. In this paper, a UAV-assisted secure communication system model is established based on non-orthogonal multiple access (NOMA) from the perspective of physical layer security, aiming to conceal the transmission behavior between UAVs and legitimate users (LUs). Specifically, a mobile UAV assumes the role of an aerial base station, leveraging NOMA technique to transmit data to LUs while evading detection from mobile eavesdropper situated on the ground. To fortify the security performance of the system, a hovering UAV acts as a friendly jammer and transmits interference signals to mobile eavesdropper (referred to as Eve). The objective of this scheme is to maximize the minimum average secure rate of all LUs by meticulously optimizing the trajectory and power allocation of the mobile UAV, subject to secrecy performance constraints. The highly interdependent and non-convex nature of this optimization problem renders direct solutions infeasible. Hence, this paper designs an efficient iterative algorithm that decouples the original problem into two subproblems, enabling an alternating optimization process for the trajectory and power allocation of the mobile UAV until the convergence of the objective function is achieved. Simulation results demonstrate that the proposed algorithm effectively improves the minimum average secure rate of all LUs compared with the benchmark scheme.

Deep reinforcement learning enabled UAV-IRS-assisted secure mobile edge computing network

The deployment of intelligent reflecting surfaces (IRS) on dynamically moving unmanned aerial vehicles (UAVs) can enhance the communication performance of mobile edge computing (MEC), improve the system flexibility, and alleviate eavesdropping on air–ground channels. In this paper, an IRS-equipped unmanned aerial vehicle (UAV)-assisted secure MEC network is proposed. By jointly optimizing the Relay-UAV stopping point, IRS-UAV stopping point, IRS reflection coefficients and the task offloading ratio, the objective of our proposed optimization scheme is to minimize the transmission delay and computing delay while considering the secure transmission performance. To solve this non-convex optimization problem with coupled variables, we propose an intelligent optimization algorithm based on dueling double deep Q networks (D3QN)-deep deterministic policy gradient (DDPG) that can efficiently explore the trajectories and a great number of the IRS reflection elements. Simulation results demonstrate that the intelligent algorithm exhibits good convergence and our proposed scheme can achieve a good balance between system consumption and secrecy rate.

Fair Energy-Efficient Resource Optimization for Green Multi-NOMA-UAV Assisted Internet of Things

Owing to the advantages of better air-ground channel and higher flexibility, unmanned aerial vehicle (UAV) has been widely applied in the field of Internet of Things (IoT) to increase the communication coverage. However, the UAV with limited energy is facing severe energy shortage when serving more and more IoT devices. In this paper, we propose a non-orthogonal multiple access (NOMA) based green multi-UAV assisted IoT system to increase user capacity while improving the energy utilization of each UAV. Considering the limited energy budget of each UAV, we formulate a fair energy-efficient resource optimization problem under the constraints of maximum transmit power of each UAV, minimum communication rate requirement of each user and UAV mobility. By alternately optimizing the communication scheduling, transmit power allocation and UAV trajectory with the Dinkelbach method and the successive convex approximation (SCA), the energy-efficient fairness can be achieved between the UAVs by maximizing the minimum energy efficiency of the UAVs. The simulation results indicate the fair energy efficiency between the UAVs can be obtained through the alternative resource optimization, and the proposed multi-NOMA-UAV assisted IoT can get higher energy efficiency than the traditional orthogonal multiple access (OMA)-UAV assisted IoT.

A DL-Based High-Precision Positioning Method in Challenging Urban Scenarios for B5G CCUAVs

Unmanned aerial vehicles (UAVs) facilitate services in civilian and industrial fields but suffer from a limited direct link operating range and unreliable satellite positioning in urban canyons. Fortunately, cellular-connected UAVs (CCUAVs) overcome these shortcomings, benefitting from the beyond 5th generation (B5G) network’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">city-level coverage</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">high-precision positioning capabilities</i> , and are considered a paradigm of 5G-advanced and beyond. However, in a challenging airspace (e.g., urban canyon), the CCUAV localization accuracy deteriorates due to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">low signal-to-interference-plus-noise (SINR) air-ground channels</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">strong multipath effects</i> . To solve these problems, we first construct channel amplitude-phase response (CAPR) images to characterize the cellular channel in a challenging airspace for CCUAV positioning. In particular, the effect of down-tilted antennas and high-dimensional channel features are embedded into CAPR images, to meet the relevant cellular communication criteria. Subsequently, a deep learning (DL) model, the scale-shared quarter network (SSQ-Net), is devised for CAPR image-based positioning, along with a robustness enhancement method. With this method, the multipath effects and interference in challenging environments are exploited to improve positioning accuracy and robustness, instead of being treated as detriments. Finally, the experimental results in a typical urban canyon show that our method outperforms state-of-the-art methods in terms of accuracy and robustness.

Application of Path-Based Multipath Component Tracking on Air-Ground Channel Measurement Data

The operation of unmanned aircraft is unthinkable without reliable wireless communication links: Despite a comparatively high expected level of autonomy of unmanned aircraft, monitoring and remote controlling are required to some degree. As the design of every communication link requires good knowledge on the characteristics of the communication channel, we have performed a measurement campaign to collect channel sounding data for the wireless air-ground channel at C-band. While we have focused on the campaign description and the analysis of the dominant signal component in previous publications, we now concentrate on the detection and tracking of multipath components. In this paper, we present our data processing chain that allows a fast parallel processing of the measurement data, as data dependencies are reduced as much as possible. We furthermore introduce a path-based multipath component tracking approach and apply it to our measurement data. This tracking allows us to estimate the location of reflectors causing multipath component signals. We apply our processing chain to data recorded during take-off at a small airport and compare the results of the reflector localization to a satellite image of the airport to successfully verify our approach.

UAV-Aided Secure Short-Packet Data Collection and Transmission

Benefiting from the deployment flexibility and the line-of-sight (LoS) channel conditions, unmanned aerial vehicle (UAV) has gained tremendous attention in data collection for wireless sensor networks. However, the high-quality air-ground channels also pose significant threats to the security of UAV-aided wireless networks. In this paper, we propose a short-packet secure UAV-aided data collection and transmission scheme to guarantee the freshness and security of the transmission from the sensors to the remote ground base station (BS). First, during the data collection phase, the trajectory, the flight duration, and the user scheduling are jointly optimized with the objective of maximizing the energy efficiency (EE). To solve the non-convex EE maximization problem, we adopt the first-order Taylor expansion to convert it into two convex subproblems, which are then solved via successive convex approximation. Furthermore, we consider the maximum rate of transmission in the UAV data transmission phase to achieve a maximum secrecy rate. The transmit power and the blocklength of UAV-to-BS transmission are jointly optimized subject to the constraints of eavesdropping rate and outage probability. Simulation results are provided to validate the effectiveness of the proposed scheme.

Covert Communication Assisted by UAV-IRS

With the benefits of unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS), they can be combined to further enhance the communication performance. However, the high-quality air-ground channel is more vulnerable to the adversarial eavesdropping. Therefore, in this paper, we propose a covert communication scheme assisted by the UAV-IRS to maximize the covert transmission rate. Specifically, the ground transmitter, Alice, secretly delivers the private message to a legitimate receiver, Bob, via the UAV-IRS, wishing that the transmission will not be observable by the warden, Willie. In addition, Willie is adversarial to Alice and the UAV-IRS, which makes his accurate location difficult to obtain. Given this fact, we first determine an optimal detection threshold and derive the error detection probability at Willie, which is the worst-case situation for the legitimate transmission. Then, we maximize the covert transmission rate by alternatively optimizing the transmit power of Alice, the IRS phase shift and the horizontal location of UAV-IRS subject to the covert requirements. Numerical results are presented to demonstrate the effectiveness of the proposed covert communication scheme assisted by UAV-IRS.

AG channel measurements and characteristics analysis in hilly scenarios for 6G UAV communications

As an important part of sixth generation (6G) integrated space-air-ground-sea networks, unmanned aerial vehicle (UAV) communications have aroused great attention and one of its typical application scenarios is the hilly environments. The related UAV air-ground (AG) channel characteristics analysis is crucial for system design and network evaluation of future UAV communications in hilly scenarios. In this paper, a recently conducted channel measurements campaign in a hilly scenario is presented, which is conducted at the center frequencies of 2.585 GHz and 3.5 GHz for different flight trajectories. Based on the measurement data, some key channel characteristics are analyzed, including path loss (PL), shadow fading (SF), Rician K-factor, root mean square (RMS) delay spread (DS), and temporal auto-correlation function (ACF). Finally, the comparison of typical channel characteristics under circular and straight trajectories is given. The related results can provide a theoretical reference for constructing future UAV communication system in hilly scenarios.

Joint HAP deployment and resource allocation for HAP‐UAV‐terrestrial integrated networks

While the terrestrial base stations (TBSs) in the fifth-generation (5G) network provide high throughput for the conventional terrestrial users (TUs), it is still challenging for the network to support massive TUs and unmanned aerial vehicles (UAVs) simultaneously due to the complicated air–ground channel and severe interference. In this paper, the deployment of a high-altitude platform (HAP) as a supplement for the terrestrial networks, in which the HAP and TBSs serve TUs and UAVs simultaneously in a joint manner, is studied. The novel network has two challenges. First, the deployment of the HAP, which is a new degree of freedom, should be optimized considering the terrestrial network. Second, the channel of the joint HAP and TBS network that serves multiple TUs and UAVs concurrently is complicated, and the resource allocation of the network should be designed. To tackle the above two challenges, a joint resource allocation and HAP deployment problem are formulated, and a gradient-and-matching-based algorithm is proposed to solve it efficiently. Simulation results show that the HAP and the proposed algorithm enhance the sum-rate of the network by over 30%, and the average data rate of both the TUs and the UAVs can be effectively improved.

KF-LSTM Based Beam Tracking for UAV-Assisted mmWave HSR Wireless Networks

Owing to the deployment flexibility, unmanned aerial vehicles (UAVs), equipped with lightweight base stations (BSs), provide a new paradigm for wireless access from the sky to temporarily complement terrestrial networks, which bring benefits to many applications, such high-speed railway (HSR) communications. In UAV-assisted wireless communications, the advantage of the line of sight (LOS) dominant air-ground channels makes it more attractive to apply directional millimeter wave (mmWave) communications, where beam management is highly related to user location. Nevertheless, due to the high mobility of UAVs and trains, how to accurately and efficiently track beams becomes a critical problem to solve. Based on this observation, in this paper, we first present an UAV-assisted dual-band HSR wireless network architecture, which integrates the frequency bands below 6 GHz (sub-6 GHz) to provide reliable transmissions for UAV controls and HSR safety services, with the mmWave bands to enhance the transmission capacity between UAVs and trains. To solve the beam tracking problem, we analyze the beam angular variations, and then propose an efficient algorithm based on Kalman filtering (KF) with varying update periods and long-short term memory (LSTM) to improve the beam tracking performance. Finally, we conduct extensive simulations to demonstrate that the proposed scheme can achieve more accurate beam tracking and higher spectrum efficiency.

A Formulation of the Log-Logistic Distribution for Fading Channel Modeling

In some scenarios, the log-logistic (LL) distribution is shown to provide the best fit to field measurements in the context of wireless channel modeling. However, a fading channel model based on the LL distribution has not been formulated yet. In this work, we introduce the L-distribution as a reformulation of the LL distribution for channel modeling purposes. We provide closed-form expressions for its PDF, CDF, and moments. Performance analysis of wireless communication systems operating under L-fading channels is exemplified, providing exact and asymptotic expressions for relevant metrics such as the outage probability and the average capacity. Finally, important practical aspects related to the use of the L-distribution for channel fitting purposes are discussed in two contexts: (i) millimeter-wave links with misaligned gain, and (ii) air–ground channels in unmanned aerial vehicle communications.

Time‐Varying Channel Estimation Based on Air‐Ground Channel Modelling and Modulated Learning Networks

To improve the time-varying channel estimation accuracy of orthogonal frequency division multiplexing air-ground datalink in complex environment, this paper proposes a time-varying air-ground channel estimation algorithm based on the modulated learning networks, termed as MB-ChanEst-TV. The algorithm integrates the modulated convolutional neural networks (MCNN) with the bidirectional long short term memory (Bi-LSTM), where the MCNN subnetworks accomplish channel interpolation in frequency domain and compress the network model while the Bi-LSTM subnetworks achieve channel prediction in time domain. Considering the unique characteristics of airframe shadowing for unmanned aircraft systems, we propose to combine the classical 2-ray channel model with the tapped delay line model and present a more realistic channel impulse response samples generation approach, whose code and dataset have been made publicly available. We demonstrate the effectiveness of our proposed approach on the generated dataset, where experimental results indicate that the MB-ChanEst-TV model outperforms existing state-of-the-art methods with a lower estimation error and better bit error ratio performance under different signal to noise ratio conditions. We also analyze the effect of roll angle of the aircraft and the duration percentage of the airframe shadow on the channel estimation.

Analysis of the Dominant Signal Component of the Air-Ground Channel Based on Measurement Data at C-Band

Operating remotely piloted aircraft is not imaginable without a continuous data exchange between the air vehicle and the remote pilot. This data exchange requires reliable data links. One approach for such a data link discussed in the community is a terrestrial system deployed in C-band. A good knowledge of the physical conditions of the communication channel, in this case the air-ground/ground-air channel, is indispensable for the development of wireless data links. Therefore we carried out a 50 MHz bandwidth channel sounding campaign with a terrestrial transmitter and an airborne receiver. In this paper we give a detailed description of our campaign setup and the processing of the collected data. The campaign covered several flight scenarios, such as take-off, taxiing, and multiple en-route maneuvers. We furthermore present results on the received power and the amplitude distribution of the dominant component of the received signal for the different flight scenarios. We observed significant drops in reception power during certain maneuvers that need to be considered in the design process of a data link for unmanned aviation. Additionally, we show that the amplitude distribution follows the distributions commonly used in statistical channel modeling of wireless channels to some extent. We finally present parameter sets for multiple flight scenarios for scaling the amplitude distributions to allow a statistical channel modeling of the reception power of the first resolvable signal path.

Covert Communication in UAV-Assisted Air-Ground Networks

Unmanned aerial vehicle (UAV) assisted communication is a promising technique for future wireless networks due to its characteristics of low cost and flexible deployment. However, the high possibility of line-of-sight (LoS) air-ground channels may result in a great risk of being attacked by malicious users. Especially compared to the encryption and physical layer security that prevent eavesdropping, covert communication aims at hiding the existence of transmission, which is able to satisfy the more critical requirement of security. Thus, in this article, we focus on the covert communication issues of UAV-assisted wireless networks. First, the preliminaries of secure communications including encryption, physical layer security and covert communication are discussed. Then, current works and typical applications of UAV in covert communications are demonstrated. We then propose two schemes to enhance the covertness of UAV-assisted networks for some typical scenarios. Specifically, to improve the covert rate in UAV-assisted data dissemination, an iterative algorithm is proposed to jointly optimize the time slot, transmit power and trajectory. For the covertness of ground-air communication, a friendly jammer is employed to confuse the wardens, where the location of the jammer, the jamming power and the legitimate transmit power are jointly optimized. Numerical results are presented to validate the performance of these two proposed schemes. Finally, several challenges and promising directions are pointed out.

UAV-Sensing-Assisted Cellular Interference Coordination: A Cognitive Radio Approach

Aerial-ground interference mitigation has been deemed as the main challenge in realizing cellular-connected unmanned aerial vehicle (UAV) communications. Due to the line-of-sight (LoS)-dominant air-ground channels, the UAV generates/suffers much stronger interference to/from cellular base stations (BSs) over a much larger region in its uplink/downlink communication, as compared to the terrestrial users. As a result, conventional inter-cell interference coordination (ICIC) techniques catered for terrestrial networks become ineffective in mitigating the more severe UAV-induced interference. To deal with this new challenge, this letter introduces a cognitive radio based solution by treating the UAV and terrestrial users as secondary and primary users in the network, respectively. In particular, the LoS channels with terrestrial BSs/users endow the UAV with a powerful spectrum sensing capability for detecting the terrestrial signals over a much larger region than its serving BS. By exploiting this unique feature, we propose a new UAV-sensing-assisted ICIC design for both the UAV downlink and uplink communications. Specifically, the UAV senses its received interference and the transmissions of terrestrial users in the downlink and uplink, respectively, over the resource blocks (RBs) available at its serving BS to assist its RB allocation to the UAV for avoiding the interference with co-channel terrestrial communications. Numerical results demonstrate that the proposed UAV-assisted ICIC outperforms the conventional terrestrial ICIC by engaging the neighboring BSs for cooperation only.

Secure Communication in NOMA-Assisted Millimeter-Wave SWIPT UAV Networks

Future wireless networks have requirements of large connections, low power consumption, high reliability, and strong security. Considering the abundant available bandwidth of millimeter wave (mmWave) and the large line-of-sight (LOS) link probability of air–ground channel, in this article, we develop a framework to study security, reliability and energy coverage performance of the downlink mmWave simultaneous wireless information and power transfer (SWIPT) unmanned aerial vehicle (UAV) networks under nonorthogonal multiple access (NOMA) and orthogonal multiple access (OMA) schemes, where a UAV serves two types of authorized Internet of Things (IoT) devices in the presence of multiple passive eavesdroppers. Directional modulation (DM) is also used to improve the physical layer security (PLS) performance. First, the analytical expressions for connection outage probability (COP), secrecy outage probability (SOP), and effective secrecy throughput (EST) of the users with high rate security requirement (HRSR) under NOMA and OMA schemes are derived using stochastic geometry. Then, we optimize the active antenna selection of DM using adaptive genetic simulated annealing algorithm (AGSA) to further improve the secrecy performance based on the obtained analytical results. Finally, the closed-form expressions for the COP and energy-information coverage probability (EICP) of the energy-constrained users with low-rate requirement (ECLR) under NOMA and OMA schemes are obtained. The numerical and simulation results provide interesting insights into the influence of various parameters on the tradeoff between the reliability and security for the HRSR user, and the tradeoff between the reliability and energy coverage for the ECLR user. Moreover, the EST of the NOMA scheme outperforms OMA at low transmit power and high codeword transmission rate.