We investigate a transmission mechanism aiming to improve the data rate between a base station (BS) and a user equipment (UE) through deploying multiple relaying UAVs. We consider the effect of interference incurred by another established communication network, which makes our problem challenging and different from the state of the art. We aim to design the 3D trajectories and power allocation for the UAVs to maximize the data flow of the network while keeping the interference on the existing communication network below a threshold. We utilize the mobility feature of the UAVs to evade the (un)-intended interference caused by (un)-intentional interferers. To this end, we propose an alternating-maximization approach to jointly obtain the 3D trajectories and the UAVs transmission powers. We handle the 3D trajectory design by resorting to spectral graph theory and subsequently address the power allocation through convex optimization techniques. We also approach the problem from the intentional interferer’s perspective where smart jammers chase the UAVs to effectively degrade the data flow of the network. We also extend our work to the case for multiple UEs. Finally, we demonstrate the efficacy of our proposed method through extensive simulations.

We propose using Carrier Sensing (CS) for distributed interference management in millimeter-wave (mmWave) cellular networks where spectrum is shared by multiple operators that do not coordinate among themselves. In addition, even the base station sites can be shared by the operators. We describe important challenges in using traditional CS in this setting and propose enhanced CS protocols to address these challenges. Using stochastic geometry, we develop a general framework for downlink coverage probability analysis of our shared mmWave network in the presence of CS and derive the downlink coverage probability expressions for several CS protocols. Our work is the first to investigate and analyze (using stochastic geometry) CS for mmWave networks with spectrum and BS sites shared among non-coordinating operators. We evaluate the downlink coverage probability of our shared mmWave network using simulations as well as numerical examples based on our analysis. Our evaluations show that our proposed approach leads to an improvement in coverage probability, compared to the coverage probability with no CS, for higher values of signal-to-interference and noise ratio (SINR). Interestingly, our evaluations also reveal that for lower values of SINR, not using any CS is the best strategy in terms of the downlink coverage probability.

This paper studies the problem of distributed beam scheduling for 5G millimeter-Wave (mm-Wave) cellular networks where base stations (BSs) belonging to different operators share the same spectrum without centralized coordination among them. Our goal is to design efficient distributed scheduling algorithms to maximize the network utility, which is a function of the achieved throughput by the user equipment (UEs), subject to the average and instantaneous power consumption constraints of the BSs. We propose a Media Access Control (MAC) and a power allocation/adaptation mechanism utilizing the Lyapunov stochastic optimization framework and non-cooperative games. In particular, we first decompose the original utility maximization problem into two sub-optimization problems for each time frame, which are a convex optimization problem and a non-convex optimization problem, respectively. By formulating the distributed scheduling problem as a non-cooperative game where each BS is a player attempting to optimize its own utility, we provide a distributed solution to the non-convex sub-optimization problem via finding the Nash Equilibrium (NE) of the game whose weights are determined optimally by the Lyapunov optimization framework. Finally, we conduct simulation under various network settings to show the effectiveness of the proposed game-based beam scheduling algorithm in comparison to that of several reference schemes.

Physical layer security (PLS) techniques can help to protect wireless networks from eavesdropper attacks. In this paper, we consider the authentication technique that uses fingerprint embedding to defend 5G cellular networks with unmanned aerial vehicle (UAV) systems from eavesdroppers and intruders. Since the millimeter wave (mmWave) cellular networks use narrow and directional beams, PLS can take further advantage of the 3D spatial dimension for improving the authentication of UAV users. Considering a multi-user mmWave cellular network, we propose a power allocation technique that jointly takes into account splitting of the transmit power between the precoder and the authentication tag, which manages both the secrecy as well as the achievable rate. Our results show that we can obtain optimal achievable rate with expected secrecy.

5G is the beginning of a new era in cellular communication, bringing up a highly connected network with the incorporation of the Internet of Things (IoT). To flexibly operate all the IoT devices over a cellular network, Device-to-Device (D2D) communication standard was developed. However, IoT devices such as drones utilizing 5G D2D services could be a perfect target for malicious attacks as they pose several safety threats if they are compromised. Furthermore, there will be heavy traffic with an increased number of IoT devices connected to the 5G core. Therefore, we propose a lightweight, fast, and reliable authentication mechanism compatible with the 5G D2D ProSe standard mechanisms. Specifically, we propose a distributed authentication with a delegation-based scheme instead of the repeated access to the 5G core network key management functions. Hence, a legitimate drone is authorized by the core network via offering a proxy signature to authenticate itself to other drones. We implemented the proposed protocol in ns-3 that supports 5G D2D-based communication. We also conducted computational calculations on the RaspberryPi3 IoT device to mimic the drone calculation process and delays. The results demonstrate that the proposed protocol is lightweight and reliable.

As the use of unmanned aerial vehicles (UAVs) in various commercial, civil, and military applications increases, it becomes important to study the design of aerial drone corridors that can support multiple simultaneous UAV missions. In this work, we study the placement of base stations (BSs) to serve aerial drone corridors while satisfying specific UAV mission requirements, such as the geometrical waypoints for the UAV to fly through and the minimum data rate to be supported along the mission trajectory. We develop a mathematical model of the drone corridor and propose a brute force algorithm that leverages A* search to meet the quality of service (QoS) requirements of the corridor by choosing the minimal set of BS locations from a pre-determined initial set. Using raytracing simulations, BS placement results are presented for various antenna array sizes in a dense urban region in East Manhattan. It was found that, for the scenario under consideration, a single BS equipped with an 8x8 antenna array is sufficient to satisfy the given QoS requirements of the corridor, while two BSs are required when using 4x4 antenna arrays.

With the wide bandwidths in millimeter wave (mmWave) frequency band that results in unprecedented accuracy, mmWave sensing has become vital for many applications, especially in autonomous vehicles (AVs). In addition, mmWave sensing has superior reliability compared to other sensing counterparts such as camera and LiDAR, which is essential for safety-critical driving. Therefore, it is critical to understand the security vulnerabilities and improve the security and reliability of mmWave sensing in AVs. To this end, we perform the end-to-end security analysis of a mmWave-based sensing system in AVs, by designing and implementing practical physical layer attack and defense strategies in a state-of-the-art mmWave testbed and an AV testbed in real-world settings. Various strategies are developed to take control of the victim AV by spoofing its mmWave sensing module, including adding fake obstacles at arbitrary locations and faking the locations of existing obstacles. Five real-world attack scenarios are constructed to spoof the victim AV and force it to make dangerous driving decisions leading to a fatal crash. Field experiments are conducted to study the impact of the various attack scenarios using a Lincoln MKZ-based AV testbed, which validate that the attacker can indeed assume control of the victim AV to compromise its security and safety. To defend the attacks, we design and implement a challenge-response authentication scheme and a RF fingerprinting scheme to reliably detect aforementioned spoofing attacks.

To address the demands for more capacity and higher data rates, the fifth generation network (5G) technology is being developed and gradually rolled out by major cellular carriers. Millimeter wave (mmWave) systems and unmanned aerial vehicles (UAVs) are two critical enablers of 5G. To ease the integration of 5G into existing networks, it is essential to study how base stations (BSs) can be used to serve both ground and aerial users simultaneously in a mmWave network. In this work, we consider BSs equipped with two antennas- one tilted down to serve ground users and another tilted up to serve aerial users. Using ray tracing simulations, we investigate the ideal tilt of these two antennas. Our results indicate that reusing the spectrum effectively in urban environments requires an understanding of the interplay between the effects of shadowing due to buildings, ground reflections, beam orientation, BS separation, interference between the two beams, and UAV heights. Specifically, to simultaneously serve UAVs at a height of 200 m and ground users, it is desirable to use a BS equipped with one antenna tilted up at 30 degrees and another tilted down at of 10 degrees. This achieves the best compromise between the above effects for the simulation configuration considered in this paper. Further, it was observed that the aerial and ground users are, in some scenarios, actually better served by the antenna not meant for them.

The integration of unmanned aerial vehicles (UAVs) into the terrestrial communications networks with a variety of tasks is viewed as a key technology for 5G and beyond. In this work, we consider the uplink millimeter-wave (mmWave) transmission between a set of UAVs and a base station (BS), where the UAVs deploy uplink non-orthogonal multiple access (NOMA) in multiple clusters. Furthermore, the BS also serves its own desired ground user equipment (UE) in the presence of many other ground UEs associated with other cells, which share the same frequency band. Considering the limited energy budget of UAVs, we formulate an energy efficiency (EE) problem, and propose a solution aided by the Dinkelbach's algorithm and successive convex approximation (SCA). Using realistic air-to-ground (A2G) and terrestrial channel models, we assess the performance of the proposed algorithm under various circumstances (maximum transmit power for UAVs, quality-of-service (QoS) constraint for the desired UE, etc.), and identify the best use cases.

The millimeter wave (mmWave) technologies can significantly increase the throughput and user capacity in the future wireless networks. In term of device authentication, due to the usage of highly directional communication link, new physical layer identification (PLI) mechanism based on the spatial-temporal beam features becomes available. However, it is not known how to implement the new PLI mechanism using commodity devices in multiple client scenario in wireless networks. To this end, this paper presents a practical operational framework for the new beam feature-based PLI that is compatible with 802.11ad/ay standards. The low cost of these commodity devices leads to much wider beams, multiple main lobes, and high side lobes which in turn results in frequent sector level sweep (SLS) even for a minimal level of the transmitter-receiver misalignment. The high mobility sensitivity also triggers SLS. The key idea is to utilize the mobility of the mmWave device to collect enough measurements, the beam pattern feature values, from different observation angles where the beam features are extracted. This mobility effect takes advantage of the rich spatial-temporal information of the feature to prevent the system from spoofing. We also propose a novel feature database refinement algorithm to strengthen the database against false accept/reject rates and increase the identification accuracy. The algorithm filters the noisy data collected in the presence of multiple-clients. The proposed operational framework is implemented in commodity 802.11ad/ay devices. We show that the proposed scheme can reach near 100% accuracy even with a minimal feature vector database in real-time scenarios.

With millimeter wave (mmWave) wireless communication envisioned to be the key enabler of next generation high data rate wireless networks, security is of paramount importance. While conventional security measures in wireless networks operate at a higher layer of the protocol stack, physical layer security utilizes unique device dependent hardware features to identify and authenticate legitimate devices. In this work, we identify that the manufacturing tolerances in the antenna arrays used in mmWave devices contribute to a beam pattern that is unique to each device, and to that end we propose a novel device fingerprinting scheme based on the unique beam pattern of different codebooks used by the mmWave devices. Specifically, we propose a fingerprinting scheme with multiple access points (APs) to take advantage of the rich spatial-temporal information of the beam pattern. We perform comprehensive experiments with commercial off-the-shelf mmWave devices to validate the reliability performance of our proposed method under various scenarios. We also compare our beam pattern feature with a conventional physical layer feature namely power spectral density feature (PSD). To that end, we implement PSD feature based fingerprinting for mmWave devices. We show that the proposed multiple APs scheme is able to achieve over 99% identification accuracy for stationary LOS and NLOS scenarios and significantly outperform the PSD feature fingerprinting method. For mobility scenario, the overall identification accuracy is 99%. In addition, we perform security analysis of our proposed beam pattern fingerprinting system and PSD fingerprinting system by studying the feasibility of performing impersonation attacks. We design and implement an impersonation attack mechanism for mmWave wireless networks using state-of-the-art 60 GHz software defined radios. We discuss our findings and their implications on the security of the mmWave wireless networks.

The deployment of the unmanned aerial vehicle (UAV) has been foreseen as a promising technology for the next generation communication networks. The distance limitation imposed by the line of sight RF connection can be removed by using RF coverage from existing commercial cellular service. In this work, we consider a transmission mechanism that aims to improve the data rate between a terrestrial base station (BS) and user equipment (UE) through deploying multiple UAVs relaying the desired data flow. Considering the coexistence of this network with other established communication networks, we take into account the effect of interference, which is incurred by the existing nodes. Our primary goal is to optimize the three-dimensional (3D) trajectories and power allocation for the relaying UAVs to maximize the data flow while keeping the interference to existing nodes below a predefined threshold. An alternating-maximization strategy is proposed to solve the joint 3D trajectory design and power allocation for the relaying UAVs. To this end, we handle the information exchange within the network by resorting to spectral graph theory and subsequently address the power allocation through convex optimization techniques. Simulation results show that our approach can considerably improve the information flow while the interference threshold constraint is met.

Using radio frequency (RF) coverage from the existing cellular networks is an attractive option to maintain beyond visual line-of-sight (BVLOS) connectivity with drones. These cellular drones can connect with a ground control station (GCS) for control and data delivery wherever cellular service is available. However, RF coverage for these cellular networks has been optimized for the ground users and while they do leak upwards, reliable RF coverage exists to only about 400 feet high. As the drones are becoming more commonly used for activities ranging from emergency responses to aerial deliveries, the base stations covering users both on the ground and in the air with RF transmission cannot scale to the capacity needed to support increasing number of drones. Use of directional beams that are necessary for millimeter wave (mmWave) transmission in the 5G cellular system can reduce interference among users, and hence increase capacity compared to sector based transmissions used for 4G systems without massive MIMO (mMIMO). In this paper, considering the multiplicative capacity gains needed to support large number of drones, we propose a separate mmWave cellular network with optimized coverage for a ''drone corridor'' in the air. We present our current findings in the following areas critical to validating the effectiveness of this proposed network: 1) RF propagation characteristics towards drones in the air compared with propagation towards users on the ground; 2) use of multiple access (MA) technology for increased spectral efficiency for a swarm of drones; 3) optimal design of protected zones and beamforming to secure drone specific wireless communications.

Internet of things (IoT) is one of main paradigms for 5G wireless systems. Due to high connection density, interference from other sources is a key problem in IoT networks. Especially, it is more difficult to find a solution to manage interference in uncoordinated networks than coordinated system. In this work, we consider 3D topology of uncoordinated IoT network and propose interference mitigation scheme with respect to 3D antenna radiation pattern. In 2D topology network, the radiation pattern of dipole antenna can be assumed as onmi-directional. We show the variance of antenna gain on dipole antenna in 3D topology, consider the simultaneous use of three orthogonal dipole antennas, and compare the system performance depending on different antenna configurations. Our simulation results show that proper altitude of IoT devices can extensively improve the system performance.

Next generation wireless communication networks utilizing 60 GHz millimeter wave (mmWave) frequency bands are expected to achieve multi-gigabit throughput with the use of highly directional phased-array antennas. These directional signal beams provide enhanced security to the legitimate networks due to the increased difficulties of eavesdropping. However, there still exists significant possibility of eavesdropping since (i) the reflections of the signal beam from ambient reflectors enables opportunistic stationary eavesdropping attacks; and (ii) carefully designed beam exploration strategy enables active nomadic eavesdropping attack. This paper discusses eavesdropper attack strategies for 802.11ad mmWave systems and provides the first analytical model to characterize the success possibility of eavesdropping in both opportunistic stationary attacks and active nomadic attacks.

This paper proposes NeuralWave, an intelligent and non-intrusive user identification system based on human gait biometrics extracted from WiFi signals. In particular, the channel state information (CSI)measurements are first collected from commodity WiFi devices. Then, a collection of data preprocessing schemes are applied to sanitize and calibrate the noisy and erroneous CSI data samples to manifest and augment the gait-induced radio-frequency (RF)signatures. Next, a 23-layer deep convolutional neural network, namely RadioNet, is developed to automatically learn the salient features from the preprocessed CSI data samples. The extracted features constitute a latent representation for the gait biometric that is discriminative enough to distinguish one person from another. Using the latent biometric representation, a softmax multi-class classifier is adopted to achieve accurate user identification. Extensive experiments in a typical indoor environment are conducted to show the effectiveness of our system. In particular, NeuralWave can achieve 87.76 ± 2.14% user identification accuracy for a group of 24 people. To the best of our knowledge, NeuralWave is the first in the literature to exploit deep learning for feature extraction and classification of physiological and behavioral gait biometrics embedded in CSI signals from commodity WiFi.