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Millimeter-wave (mmWave) networks, integral to 5G communication, offer a vast spectrum that addresses the issue of spectrum scarcity and enhances peak rate and capacity. However, their dense deployment, necessary to counteract propagation losses, leads to high power consumption. An effective strategy to reduce this energy consumption in mobile networks is the sleep mode optimization (SMO) of base stations (BSs). In this paper, we propose a novel SMO approach for mmWave BSs in a 3D urban environment. This approach, which incorporates a neural network (NN) based contextual multi-armed bandit (C-MAB) with an epsilon decay algorithm, accommodates the dynamic and diverse traffic of user equipment (UE) by clustering the UEs in their respective tracking areas (TAs). Our strategy includes beamforming, which helps reduce energy consumption from the UE side, while SMO minimizes energy use from the BS perspective. We extended our investigation to include Random, Epsilon Greedy, Upper Confidence Bound (UCB), and Load Based sleep mode (SM) strategies. We compared the performance of our proposed C-MAB based SM algorithm with those of All On and other alternative approaches. Simulation results show that our proposed method outperforms all other SM strategies in terms of the $10^{th}$ percentile of user rate and average throughput while demonstrating comparable average throughput to the All On approach. Importantly, it outperforms all approaches in terms of energy efficiency (EE).

The use of unmanned aerial vehicles (UAVs) for a variety of commercial, civilian, and defense applications has increased many folds in recent years. While UAVs are expected to transform future air operations, there are instances where they can be used for malicious purposes. In this context, the detection, classification, and tracking (DCT) of UAVs (DCT-U) for safety and surveillance of national air space is a challenging task when compared to DCT of manned aerial vehicles. In this survey, we discuss the threats and challenges from malicious UAVs and we subsequently study three radio frequency (RF)-based systems for DCT-U. These RF-based systems include radars, communication systems, and RF analyzers. Radar systems are further divided into conventional and modern radar systems, while communication systems can be used for joint communications and sensing (JC&S) in active mode and act as a source of illumination to passive radars for DCT-U. The limitations of the three RF-based systems are also provided. The survey briefly discusses non-RF systems for DCT-U and their limitations. Future directions based on the lessons learned are provided at the end of the survey., Comment: This paper is under review for IEEE COMST. arXiv admin note: text overlap with arXiv:2211.10038

With the ever-increasing demand for high-speed wireless data transmission, beamforming techniques have been proven to be crucial in improving the data rate and the signal-to-noise ratio (SNR) at the receiver. However, they require feedback mechanisms that need an overhead of information and increase the system complexity, potentially challenging the efficiency and capacity of modern wireless networks. This paper investigates novel index-based feedback mechanisms that aim at reducing the beamforming feedback overhead in Wi-Fi links. The proposed methods mitigate the overhead by generating a set of candidate beamforming vectors using an unsupervised learning-based framework. The amount of feedback information required is thus reduced by using the index of the candidate as feedback instead of transmitting the entire beamforming matrix. We explore several methods that consider different representations of the data in the candidate set. In particular, we propose five different ways to generate and represent the candidate sets that consider the covariance matrices of the channel, serialize the feedback matrix, and account for the effective distance, among others. Additionally, we also discuss the implications of using partial information in the compressed beamforming feedback on the link performance and compare it with the newly proposed index-based methods. Extensive IEEE 802.11 standard-compliant simulation results show that the proposed methods effectively minimize the feedback overhead, enhancing the throughput while maintaining an adequate link performance.

Compressed beamforming algorithm is used in the current Wi-Fi standard to reduce the beamforming feedback overhead (BFO). However, with each new amendment of the standard the number of supported antennas in Wi-Fi devices increases, leading to increased BFO and hampering the throughput despite using compressed beamforming. In this paper, a novel index-based method is presented to reduce the BFO in Wi-Fi links. In particular, a k-means clustering-based approach is presented to generate candidate beamforming feedback matrices, thereby reducing the BFO to only the index of the said candidate matrices. With extensive simulation results, we compare the newly proposed method with the IEEE 802.11be baseline and our previously published index-based method. We show approximately 54% gain in throughput at high signal-to-noise (SNR) against the IEEE 802.11be baseline. Our comparison also shows approximately 4 dB gain compared to our previously published method at the packet-error-rate (PER) of 0.01 using MCS index 11. Additionally, we also discuss the impact of the distance metric chosen for clustering as well as candidate selection on the link performance.

In this paper, we consider a heterogeneous repository of drone-enabled aerial base stations with varying transmit powers that provide downlink wireless coverage for ground users. One particular challenge is optimal selection and deployment of a subset of available drone base stations (DBSs) to satisfy the downlink data rate requirements while minimizing the overall power consumption. In order to address this challenge, we formulate an optimization problem to select the best subset of available DBSs so as to guarantee wireless coverage with some acceptable transmission rate in the downlink path. In addition to the selection of DBSs, we determine their 3D position so as to minimize their overall power consumption. Moreover, assuming that the DBSs operate in the same frequency band, we develop a novel and computationally efficient beamforming method to alleviate the inter-cell interference impact on the downlink. We propose a Kalai-Smorodinsky bargaining solution to determine the optimal beamforming strategy in the downlink path to compensate for the impairment caused by the interference. Simulation results demonstrate the effectiveness of the proposed solution and provide valuable insights into the performance of the heterogeneous drone-based small cell networks., Comment: 12 pages, 7 figures. arXiv admin note: text overlap with arXiv:1804.08415 by other authors

Radio dynamic zones (RDZs) are geographical areas within which dedicated spectrum resources are monitored and controlled to enable the development and testing of new spectrum technologies. Real-time spectrum awareness within an RDZ is critical for preventing interference with nearby incumbent users of the spectrum. In this paper, we consider a 3D RDZ scenario and propose to use unmanned aerial vehicles (UAVs) equipped with spectrum sensors to create and maintain a 3D radio map of received signal power from different sources within the RDZ. In particular, we introduce a 3D Kriging interpolation technique that uses realistic 3D correlation models of the signal power extracted from extensive measurements carried out at the NSF AERPAW platform. Using C-Band signal measurements by a UAV at altitudes between 30 m-110 m, we first develop realistic propagation models on air-to-ground path loss, shadowing, spatial correlation, and semi-variogram, while taking into account the knowledge of antenna radiation patterns and ground reflection. Subsequently, we generate a 3D radio map of a signal source within the RDZ using the Kriging interpolation and evaluate its sensitivity to the number of measurements used and their spatial distribution. Our results show that the proposed 3D Kriging interpolation technique provides significantly better radio maps when compared with an approach that assumes perfect knowledge of path loss.

The emerging 5G and future 6G technologies are envisioned to provide higher bandwidths and coverage using millimeter wave (mmWave) and sub-Terahertz (THz) frequency bands. The growing demand for higher data rates using these bands can be addressed by overcoming high path loss, especially for non-line-of-sight (NLOS) scenarios. In this work, we investigate the use of passive transparent reflectors to improve signal coverage in an NLOS indoor scenario. Measurements are conducted to characterize the maximum reflectivity property of the transparent reflector using channel sounder equipment from NI. Flat and curved reflectors, each with a size of 16 inches by 16 inches, are used to study coverage improvements with different reflector shapes and orientations. The measurement results using passive metallic reflectors are also compared with the ray-tracing-based simulations, to further corroborate our inferences. The analysis reveals that the transparent reflector outperforms the metal reflector and increases the radio propagation coverage in all three frequencies of interest: 28~GHz, 39~GHz, and 120~GHz. Using transparent reflectors, there is an increase in peak received power that is greater than 5~dB for certain scenarios compared to metallic reflectors when used in flat mode, and greater than 3~dB when used in curved (convex) mode.

This paper investigates an interference-aware joint path planning and power allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in a sparse suburban environment. The UAV's goal is to fly from an initial point and reach a destination point by moving along the cells to guarantee the required quality of service (QoS). In particular, the UAV aims to maximize its uplink throughput and minimize the level of interference to the ground user equipment (UEs) connected to the neighbor cellular BSs, considering the shortest path and flight resource limitation. Expert knowledge is used to experience the scenario and define the desired behavior for the sake of the agent (i.e., UAV) training. To solve the problem, an apprenticeship learning method is utilized via inverse reinforcement learning (IRL) based on both Q-learning and deep reinforcement learning (DRL). The performance of this method is compared to learning from a demonstration technique called behavioral cloning (BC) using a supervised learning approach. Simulation and numerical results show that the proposed approach can achieve expert-level performance. We also demonstrate that, unlike the BC technique, the performance of our proposed approach does not degrade in unseen situations.

Advancements in unmanned aerial vehicle (UAV) technology have led to their increased utilization in various commercial and military applications. One such application is signal source search and localization (SSSL) using UAVs, which offers significant benefits over traditional ground-based methods due to improved RF signal reception at higher altitudes and inherent autonomous 3D navigation capabilities. Nevertheless, practical considerations such as propagation models and antenna patterns are frequently neglected in simulation-based studies in the literature. In this work, we address these limitations by using a two-ray channel model and a dipole antenna pattern to develop a simulator that more closely represents real-world radio signal strength (RSS) observations at a UAV. We then examine and compare the performance of previously proposed linear least square (LLS) based localization techniques using UAVs for SSSL. Localization of radio frequency (RF) signal sources is assessed based on two main criteria: 1) achieving the highest possible accuracy and 2) localizing the target as quickly as possible with reasonable accuracy. Various mission types, such as those requiring precise localization like identifying hostile troops, and those demanding rapid localization like search and rescue operations during disasters, have been previously investigated. In this paper, the efficacy of the proposed localization approaches is examined based on these two main localization requirements through computer simulations., Comment: 7 Pages, submitted to 2023 PIMRC

Channel rank and condition number of multi-input multi-output (MIMO) channels can be effective indicators of achievable rates with spatial multiplexing in mobile networks. In this paper, we use extensive ray tracing simulations to investigate channel rank, condition number, and signal coverage distribution for air-to-ground MIMO channels. We consider UAV-based user equipment (UE) at altitudes of 3 m, 30 m, 70 m, and 110 m from the ground. Moreover, we also consider their communication link with a cellular base station in urban and rural areas. In particular, Centennial Campus and Lake Wheeler Road Field Labs of NC State University are considered, and their geographical information extracted from the open street map (OSM) database is incorporated into ray tracing simulations. Our results characterize how the channel rank tends to reduce as a function of UAV altitude, while also providing insights into the effects of geography, building distribution, and threshold parameters on channel rank and condition number.

In this study, we propose a low-cost and portable millimeter-wave software-defined radio (SDR) for wireless experimentation in the 60 GHz band. The proposed SDR uses Xilinx RFSoC2x2 and Sivers EVK06002 homodyne transceiver and provides a TCP/IP-based interface for companion computer (CC)-based baseband signal processing. To address the large difference between the processing speed of the CC and the sample rate of analog-to-digital converters, we propose a method, called waveform-triggered reception (WTR), where a hard-coded block detects a special trigger waveform to acquire a pre-determined number of IQ samples upon the detection. We also introduce a buffer mechanism to support discontinuous transmissions. By utilizing the WTR along with discontinuous transmissions, we conduct a beam sweeping experiment, where we evaluate 4096 beam pairs rapidly without compromising the flexibility of the CC-based processing. We also generate a dataset that allows one to calculate physical layer parameters such as signal-to-noise ratio and channel frequency response for a given pair of transmit and receive beam indices., Comment: Accepted for presentation at IEEE INFOCOM CNERT: Computer and Networking Experimental Research using Testbeds Workshop 2023, 6 figures, corresponding GitHub page: https://github.com/alphansahin/mmWaveSDR

In this paper, we consider an indoor hybrid visible light communication (VLC) and radio frequency (RF) communication scenario with two-hop downlink transmission. The LED carries both data and energy in the first phase, VLC, to an energy harvester relay node, which then uses the harvested energy to re-transmit the decoded information to the RF user in the second phase, RF communication. The direct current (DC) bias and the assigned time duration for VLC transmission are taken into account as design parameters. The optimization problem is formulated to maximize the data rate with the assumption of decode-and-forward relaying for fixed receiver orientation. The non-convex optimization is split into two sub-problems and solved cyclically. It optimizes the data rate by solving two sub-problems: fixing time duration for VLC link to solve DC bias and fixing DC bias to solve time duration. The effect of random receiver orientation on the data rate is also studied, and closed-form expressions for both VLC and RF data rates are derived. The optimization is solved through an exhaustive search, and the results show that a higher data rate can be achieved by solving the joint problem of DC bias and time duration compared to solely optimizing the DC bias.

With its promise of increasing softwarization, improving disaggregability, and creating an open-source based ecosystem in the area of Radio Access Networks, the idea of Open RAN has generated rising interest in the community. Even as the community races to provide and verify complete Open RAN systems, the importance of verification of systems based on Open RAN under real-world conditions has become clear, and testbed facilities for general use have been envisioned, in addition to private testing facilities. Aerial robots, including autonomous ones, are among the increasingly important and interesting clients of RAN systems, but also present a challenge for testbeds. Based on our experience in architecting and operating an advanced wireless testbed with aerial robots as a primary citizen, we present considerations relevant to the design of Open RAN testbeds, with particular attention to making such a testbed capable of controlled experimentation with aerial clients. We also present representative results from the NSF AERPAW testbed on Open RAN slicing, programmable vehicles, and programmable radios.

Localization of a radio frequency (RF) signal source has various use cases, ranging from search and rescue, identification and deactivation of jammers, and tracking hostile activity near borders or on the battlefield. The use of unmanned aerial vehicles (UAVs) for signal source search and localization (SSSL) can have significant advantages when compared to terrestrial-based approaches, due to the ease of capturing RF signals at higher altitudes and the autonomous 3D navigation capabilities of UAVs. However, the limited flight duration of UAVs due to battery constraints, as well as limited computational resources on board of lightweight UAVs introduce challenges for SSSL. In this paper, we study various SSSL techniques using a UAV with predefined waypoints. A linear least square (LLS) based localization scheme is considered with enhanced reference selection due to its relatively lower computational complexity. Five different LLS localization algorithms are proposed and studied for selecting anchor positions to be used for localization as the UAV navigates through an area. The performance of each algorithm is measured in two ways: 1) real-time positioning accuracy during the ongoing UAV flight, and 2) long-term accuracy measured at the end of the UAV flight. We compare and analyze the performance of the proposed approaches using computer simulations in terms of accuracy, UAV flight distance, and reliability., Comment: 6 pages, 6 figures, Submitted to 2023 IEEE VTC

Due to the scarcity of spectrum resources, the emergence of new technologies and ever-increasing number of wireless devices operating in the radio frequency spectrum lead to data congestion and interference. In this work, we study the effect of altitude on sub-6 GHz spectrum measurement results obtained at a Helikite flying over two distinct scenarios; i.e., urban and rural environments. Specifically, we aim at investigating the spectrum occupancy of various long-term evolution (LTE), $5^{\text{th}}$ generation (5G) and citizens broadband radio service (CBRS) bands utilized in the United States for both uplink and downlink at altitudes up to 180 meters. Our results reveal that generally the mean value of the measured power increases as the altitude increases where the line-of-sight links with nearby base stations is more available. SigMF-compliant spectrum measurement datasets used in this paper covering all the bands between 100~MHz to 6~GHz are also provided.

Unmanned aerial vehicles (UAVs) are expected to be used extensively in the future for various applications, either as user equipment (UEs) connected to a cellular wireless network, or as an infrastructure extension of an existing wireless network to serve other UEs. Next generation wireless networks will consider the use of UAVs for joint communication and radar and/or as dedicated radars for various sensing applications. Increasing number of UAVs will naturally result in larger number of communication and/or radar links that may cause interference to nearby networks, exacerbated further by the higher likelihood of line-of-sight signal propagation from UAVs even to distant receivers. With all these, it is critical to study network coexistence of UAV-mounted base stations (BSs) and radar transceivers. In this paper, using stochastic geometry, we derive closed-form expressions to characterize the performance of coexisting UAV radar and communication networks for spectrum overlay multiple access (SOMA) and time-division multiple access (TDMA). We evaluate successful ranging probability (SRP) and the transmission capacity (TC) and compare the performance of TDMA and SOMA. Our results show that SOMA can outperform TDMA on both SRP and TC when the node density of active UAV-radars is larger than the node density of UAV-comms.

The use of unmanned aerial vehicles (UAVs) for different applications has increased many folds in recent years. The UAVs are expected to change the future air operations. However, there are instances where the UAVs can be used for malicious purposes. The detection, tracking, and classification of UAVs is challenging compared to manned aerial vehicles (MAVs) mainly due to small size, complex shapes, and ability to fly close to the terrain and in autonomous flight patterns in swarms. In this survey, we will discuss current and future aerial threats, and provide an overview of radar systems to counter such threats. We also study the performance parameters of radar systems for the detection, tracking, and classification of UAVs compared to MAVs. In addition to dedicated radar systems, we review the use of joint communication-radar (JCR) systems, as well as passive monitoring of changes in the common communication signals, e.g., FM, LTE, and any transmissions that may radiate from a UAV, for the detection, tracking, and classification of UAVs are provided. Finally, limitations of radar systems and comparison with other techniques that do not rely on radars for detection, tracking, and classification of aerial threats are provided., Comment: Paper under review for IEEE Communications Surveys and Tutorials

To ensure reliable and effective mobility management for aerial user equipment (UE), estimating the speed of cellular-connected unmanned aerial vehicles (UAVs) carries critical importance since this can help to improve the quality of service of the cellular network. The 3GPP LTE standard uses the number of handovers made by a UE during a predefined time period to estimate the speed and the mobility state efficiently. In this paper, we introduce an approximation to the probability mass function of handover count (HOC) as a function of a cellular-connected UAV's height and velocity, HOC measurement time window, and different ground base station (GBS) densities. Afterward, we derive the Cramer-Rao lower bound (CRLB) for the speed estimate of a UAV, and also provide a simple biased estimator for the UAV's speed which depends on the GBS density and HOC measurement period. Interestingly, for a low time-to-trigger (TTT) parameter, the biased estimator turns into a minimum variance unbiased estimator (MVUE). By exploiting this speed estimator, we study the problem of detecting the mobility state of a UAV as low, medium, or high mobility as per the LTE specifications. Using CRLBs and our proposed MVUE, we characterize the accuracy improvement in speed estimation and mobility state detection as the GBS density and the HOC measurement window increase. Our analysis also shows that the accuracy of the proposed estimator does not vary significantly with respect to the TTT parameter., Comment: Submitted to IEEE TVT. arXiv admin note: text overlap with arXiv:2002.06657

In this paper, we consider an indoor downlink dual-hop hybrid visible light communication (VLC)/radio frequency (RF) scenario. For each transmission block, we dynamically allocate a portion of time resources to VLC and the other portion to RF transmission. In the first phase (i.e., VLC transmission), the LED carries both data and energy to an energy harvester relay node. In the second phase (i.e., RF communication), the relay utilizes the harvested energy to re-transmit the decoded information to the far RF user. During this phase, the LED continues to transmit power (no information) to the relay node, aiming to harvest energy that can be used in the next transmission block. We formulate the optimization problem in the sense of maximizing the data rate under the assumption of decode-and-forward (DF) relaying. As the design parameters, the direct current (DC) bias and the assigned time duration for VLC transmission are taken into account. In particular, the joint non-convex optimization is split into two sub-problems, which are then cyclically solved. In the first sub-problem, we fix the assigned time duration to VLC link and utilize the majorization-minimization (MM) procedure to solve the non-convex DC bias problem. In the second sub-problem, we fix the DC bias obtained in the previous step and solve the optimization problem for the assigned VLC link time duration. Our results demonstrate that a higher data rate can be achieved by solving the joint problem of DC bias and time duration compared to solely optimizing the DC bias.

One of the critical challenges of operating with the terahertz or millimeter-wave wireless networks is the necessity of at least a strong non-line-of-sight (NLoS) reflected path to form a stable link. Recent studies have shown that an economical way of enhancing/improving these NLoS links is by using passive metallic reflectors that provide strong reflections. However, despite its inherent radio advantage, metals can dramatically influence the landscape's appearance - especially the indoor environment. A conceptual view of escaping this is by using transparent reflectors. In this work, for the very first time, we evaluate the wireless propagation characteristics of passive transparent reflectors in an indoor environment at 28 GHz, 39 GHz, 120 GHz, and 144 GHz bands. In particular, we investigate the penetration loss and the reflection characteristics at different frequencies and compare them against the other common indoor materials such as ceiling tile, clear glass, drywall, plywood, and metal. The measurement results suggest that the transparent reflector, apart from an obvious advantage of transparency, has a higher penetration loss than the common indoor materials (excluding metal) and performs similarly to metal in terms of reflection. Our experimental results directly translate to better reflection performance and preserving the radio waves within the environment than common indoor materials, with potential applications in controlled wireless communication., Comment: To appear in IEEE ICC Workshops 2022

The IEEE 802.11 standard based wireless local area networks (WLANs) or Wi-Fi networks are critical to provide internet access in today's world. The increasing demand for high data rate in Wi-Fi networks has led to several advancements in the 802.11 standard. Supporting MIMO transmissions with higher number of transmit antennas operating on wider bandwidths is one of the key capabilities for reaching higher throughput. However, the increase in sounding feedback overhead due to higher number of transmit antennas may significantly curb the throughput gain. In this paper, we develop an unsupervised learning-based method to reduce the sounding duration in a Wi-Fi MIMO link. Simulation results show that our method uses approximately only 8% of the number of bits required by the existing feedback mechanism and it can boost the system throughput by up to 52%.

By serving as an analog to traffic signal lights, communication signaling for drone to drone communications holds the key to the success of advanced air mobility (AAM) in both urban and rural settings. Deployment of AAM applications such as air taxis and air ambulances, especially at large-scale, requires a reliable channel for a point-to-point and broadcast communication between two or more aircraft. Achieving such high reliability, in a highly mobile environment, requires communication systems designed for agility and efficiency. This paper presents the foundations for establishing and maintaining a reliable communication channel among multiple aircraft in unique AAM settings. Subsequently, it presents concepts and results on wireless coverage and mobility for AAM services using cellular networks as a ground network infrastructure. Finally, we analyze the wireless localization performance at 3D AAM corridors when cellular networks are utilized, considering different corridor heights and base station densities. We highlight future research directions and open problems to improve wireless coverage and localization throughout the manuscript.

This work investigates the problem of unmanned aerial vehicles (UAVs) identification using their radar crosssection (RCS) signature. The RCS of six commercial UAVs are measured at 15 GHz and 25 GHz in an anechoic chamber, for both vertical-vertical and horizontal-horizontal polarization. The RCS signatures are used to train 15 different classification algorithms, each belonging to one of three different categories: statistical learning (SL), machine learning (ML), and deep learning (DL). The study shows that while the classification accuracy of all the algorithms increases with the signal-to-noise ratio (SNR), the ML algorithm achieved better accuracy than the SL and DL algorithms. For example, the classification tree ML achieves an accuracy of 98.66% at 3 dB SNR using the 15 GHz VV-polarized RCS test data from the UAVs. We investigate the classification accuracy using Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. On average, the accuracy of the classification tree ML model performed better than the other algorithms, followed by the Peter Swerling statistical models and the discriminant analysis ML model. In general, the classification accuracy of the ML and SL algorithms outperformed the DL algorithms (Squeezenet, Googlenet, Nasnet, and Resnet 101) considered in the study. Furthermore, the computational time of each algorithm is analyzed. The study concludes that while the SL algorithms achieved good classification accuracy, the computational time was relatively long when compared to the ML and DL algorithms. Also, the study shows that the classification tree achieved the fastest average classification time of about 0.46 ms., Comment: 17 pages

The stealth technology and unmanned aerial vehicles (UAVs) are expected to dominate current and future aerial warfare. The radar systems at their maximum operating ranges, however, are not always able to detect stealth and small UAVs mainly due to their small radar cross-sections and/or low altitudes. In this paper, a novel technique as an alternative to radar technology is proposed. The proposed approach is based on creating a mesh structure of laser beams initiated from aerial platforms towards the ground. The laser mesh acts as a virtual net in the sky. Any aerial vehicle disrupting the path of the laser beams are detected and subsequently localized and tracked. As an additional feature, steering of the beams can be used for increased coverage and improved localization and classification performance. A database of different types of aerial vehicles is created artificially based on Gaussian distributions. The database is used to develop several machine learning (ML) models using different algorithms to classify a target. Overall, we demonstrated through simulations that our proposed model achieves simultaneous detection, classification, localization, and tracking of a target., Comment: Submitted to ITU-JFET Journal. Under revision

The high attenuation of millimeter-wave (mmWave) would significantly reduce the coverage areas, and hence it is critical to study the propagation characteristics of mmWave in multiple deployment scenarios. In this work, we investigated the propagation and scattering behavior of 60 GHz mmWave signals in outdoor environments at a travel distance of 98 m for an aerial link (rooftop to rooftop), and 147 m for a ground link (light-pole to light-pole). Measurements were carried out using Facebook Terragraph (TG) radios. Results include received power, path loss, signal-to-noise ratio (SNR), and root mean square (RMS) delay spread for all beamforming directions supported by the antenna array. Strong line-of-sight (LOS) propagation exists in both links. We also observed rich multipath components (MPCs) due to edge scatterings in the aerial link, while only LOS and ground reflection MPCs in the other link., Comment: 3 pages, submitted to IEEE Radio & Wireless Symposium, under review

Extensive use of unmanned aerial vehicles (UAVs) is expected to raise privacy and security concerns among individuals and communities. In this context, the detection and localization of UAVs will be critical for maintaining safe and secure airspace in the future. In this work, Keysight N6854A radio frequency (RF) sensors are used to detect and locate a UAV by passively monitoring the signals emitted from the UAV. First, the Keysight sensor detects the UAV by comparing the received RF signature with various other UAVs' RF signatures in the Keysight database using an envelope detection algorithm. Afterward, time difference of arrival (TDoA) based localization is performed by a central controller using the sensor data, and the drone is localized with some error. To mitigate the localization error, implementation of an extended Kalman filter~(EKF) is proposed in this study. The performance of the proposed approach is evaluated on a realistic experimental dataset. EKF requires basic assumptions on the type of motion throughout the trajectory, i.e., the movement of the object is assumed to fit some motion model~(MM) such as constant velocity (CV), constant acceleration (CA), and constant turn (CT). In the experiments, an arbitrary trajectory is followed, therefore it is not feasible to fit the whole trajectory into a single MM. Consequently, the trajectory is segmented into sub-parts and a different MM is assumed in each segment while building the EKF model. Simulation results demonstrate an improvement in error statistics when EKF is used if the MM assumption aligns with the real motion.

The utilization of millimeter-wave (mmWave) bands in 5G networks poses new challenges to network planning. Vulnerability to blockages at mmWave bands can cause coverage holes (CHs) in the radio environment, leading to radio link failure when a user enters these CHs. Detection of the CHs carries critical importance so that necessary remedies can be introduced to improve coverage. In this letter, we propose a novel approach to identify the CHs in an unsupervised fashion using a state-of-the-art manifold learning technique: uniform manifold approximation and projection. The key idea is to preserve the local-connectedness structure inherent in the collected unlabelled channel samples, such that the CHs from the service area are detectable. Our results on the DeepMIMO dataset scenario demonstrate that the proposed method can learn the structure within the data samples and provide visual holes in the low-dimensional embedding while preserving the CH boundaries. Once the CH boundary is determined in the low-dimensional embedding, channel-based localization techniques can be applied to these samples to obtain the geographical boundaries of the CHs., Comment: This paper appears in: IEEE Communications Letters

To integrate unmanned aerial vehicles (UAVs) in future large-scale deployments, a new wireless communication paradigm, namely, the cellular-connected UAV has recently attracted interest. However, the line-of-sight dominant air-to-ground channels along with the antenna pattern of the cellular ground base stations (GBSs) introduce critical interference issues in cellular-connected UAV communications. In particular, the complex antenna pattern and the ground reflection (GR) from the down-tilted antennas create both coverage holes and patchy coverage for the UAVs in the sky, which leads to unreliable connectivity from the underlying cellular network. To overcome these challenges, in this paper, we propose a new cellular architecture that employs an extra set of co-channel antennas oriented towards the sky to support UAVs on top of the existing down-tilted antennas for ground user equipment (GUE). To model the GR stemming from the down-tilted antennas, we propose a path-loss model, which takes both antenna radiation pattern and configuration into account. Next, we formulate an optimization problem to maximize the minimum signal-to-interference ratio (SIR) of the UAVs by tuning the up-tilt (UT) angles of the up-tilted antennas. Since this is an NP-hard problem, we propose a genetic algorithm (GA) based heuristic method to optimize the UT angles of these antennas. After obtaining the optimal UT angles, we integrate the 3GPP Release-10 specified enhanced inter-cell interference coordination (eICIC) to reduce the interference stemming from the down-tilted antennas. Our simulation results based on the hexagonal cell layout show that the proposed interference mitigation method can ensure higher minimum SIRs for the UAVs over baseline methods while creating minimal impact on the SIR of GUEs., Comment: Accepted for publication in ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 2, Pages 165-185

Multiple-input multiple-output (MIMO) techniques can help in scaling the achievable air-to-ground (A2G) channel capacity while communicating with drones. However, spatial multiplexing with drones suffers from rank deficient channels due to the unobstructed line-of-sight (LoS), especially in millimeter-wave (mmWave) frequencies that use narrow beams. One possible solution is utilizing low-cost and low-complexity metamaterial-based intelligent reflecting surfaces (IRS) to enrich the multipath environment, taking into account that the drones are restricted to fly only within well-defined drone corridors. A hurdle with this solution is placing the IRSs optimally. In this study, we propose an approach for IRS placement with a goal to improve the spatial multiplexing gains, and hence to maximize the average channel capacity in a predefined drone corridor. Our results at 6 GHz, 28 GHz and 60 GHz show that the proposed approach increases the average rates for all frequency bands for a given drone corridor, when compared with the environment where there are no IRSs present, and IRS-aided channels perform close to each other at sub-6 and mmWave bands., Comment: 14 pages, 4 figures, journal article

The use of supervised learning with various sensing techniques such as audio, visual imaging, thermal sensing, RADAR, and radio frequency (RF) have been widely applied in the detection of unmanned aerial vehicles (UAV) in an environment. However, little or no attention has been given to the application of unsupervised or semi-supervised algorithms for UAV detection. In this paper, we proposed a semi-supervised technique and architecture for detecting UAVs in an environment by exploiting the RF signals (i.e., fingerprints) between a UAV and its flight-controller communication under wireless inference such as Bluetooth and WiFi. By decomposing the RF signals using a two-level wavelet packet transform, we estimated the second moment statistic (i.e., variance) of the coefficients in each packet as a feature set. We developed a local outlier factor model as the UAV detection algorithm using the coefficient variances of the wavelet packets from WiFi and Bluetooth signals. When detecting the presence of RF-based UAV, we achieved an accuracy of 96.7$\%$ and 86$\%$ at a signal-to-noise ratio of 30~dB and 18~dB, respectively. The application of this approach is not limited to UAV detection as it can be extended to the detection of rogue RF devices in an environment., Comment: 6 pages

Millimeter-wave (mmWave) and terahertz (THz) spectrum can support significantly higher data rates compared to lower frequency bands and hence are being actively considered for 5G wireless networks and beyond. These bands have high free-space path loss (FSPL) in line-of-sight (LOS) propagation due to their shorter wavelength. Moreover, in non-line-of-sight (NLOS) scenario, these two bands suffer higher penetration loss than lower frequency bands which could seriously affect the network coverage. It is therefore critical to study the NLOS penetration loss introduced by different building materials at mmWave and THz bands, to help establish link budgets for an accurate performance analysis in indoor environments. In this work, we measured the penetration loss and the attenuation of several common constructional materials at mmWave (28 and 39 GHz) and sub-THz (120 and 144 GHz) bands. Measurements were conducted using a channel sounder based on NI PXI platforms. Results show that the penetration loss changes extensively based on the frequency and the material properties, ranging from 0.401 dB for ceiling tile at 28 GHz, to 16.608 dB for plywood at 144 GHz. Ceiling tile has the lowest measured attenuation at 28 GHz, while clear glass has the highest attenuation of 27.633 dB/cm at 144 GHz. As expected, the penetration loss and attenuation increased with frequency for all the tested materials.

This paper presents a radar cross-section (RCS)-based statistical recognition system for identifying/ classifying unmanned aerial vehicles (UAVs) at microwave frequencies. First, the paper presents the results of the vertical (VV) and horizontal (HH) polarization RCS measurement of six commercial UAVs at 15 GHz and 25 GHz in a compact range anechoic chamber. The measurement results show that the average RCS of the UAVs depends on shape, size, material composition of the target UAV as well as the azimuth angle, frequency, and polarization of the illuminating radar. Afterward, radar characterization of the target UAVs is achieved by fitting the RCS measurement data to 11 different statistical models. From the model selection analysis, we observe that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively poorly. The best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. The performance of the UAV statistical recognition system is evaluated at different signal noise ratio (SNR) with the aid of Monte Carlo analysis. At an SNR of 10 dB, the average classification accuracy of 97.43% or better is achievable., Comment: one column, 39 pages

In this work, we performed a thorough comparative analysis on a radio frequency (RF) based drone detection and identification system (DDI) under wireless interference, such as WiFi and Bluetooth, by using machine learning algorithms, and a pre-trained convolutional neural network-based algorithm called SqueezeNet, as classifiers. In RF signal fingerprinting research, the transient and steady state of the signals can be used to extract a unique signature from an RF signal. By exploiting the RF control signals from unmanned aerial vehicles (UAVs) for DDI, we considered each state of the signals separately for feature extraction and compared the pros and cons for drone detection and identification. Using various categories of wavelet transforms (discrete wavelet transform, continuous wavelet transform, and wavelet scattering transform) for extracting features from the signals, we built different models using these features. We studied the performance of these models under different signal to noise ratio (SNR) levels. By using the wavelet scattering transform to extract signatures (scattergrams) from the steady state of the RF signals at 30 dB SNR, and using these scattergrams to train SqueezeNet, we achieved an accuracy of 98.9% at 10 dB SNR., Comment: Single column, 34 pages

In the upcoming 5G communication, the millimeter-wave (mmWave) technology will play an important role due to its large bandwidth and high data rate. However, mmWave frequencies have higher free-space path loss (FSPL) in line-of-sight (LOS) propagation compared to the currently used sub-6 GHz frequencies. What is more, in non-line-of-sight (NLOS) propagation, the attenuation of mmWave is larger compared to the lower frequencies, which can seriously degrade the performance. It is therefore necessary to investigate mmWave propagation characteristics for a given deployment scenario to understand coverage and rate performance for that environment. In this paper, we focus on 28 GHz wideband mmWave signal propagation characteristics at Johnston Regional Airport (JNX), a local airport near Raleigh, NC. To collect data, we use an NI PXI based channel sounder at 28 GHz for indoor, outdoor, and indoor-to-outdoor scenarios. Results on LOS propagation, reflection, penetration, signal coverage, and multi-path components (MPCs) show a lower indoor FSPL, a richer scattering, and a better coverage compared to outdoor. We also observe high indoor-to-outdoor propagation losses.

The use of millimeter-wave (mmWave) bands in 5G networks introduce a new set of challenges to network planning. Vulnerability to blockages and high path loss at mmWave frequencies require careful planning of the network to achieve the desired service quality. In this paper, we propose a novel 3D geometry-based framework for deploying mmWave base stations (gNBs) in urban environments by considering first-order reflection effects. We also provide a solution for the optimum deployment of passive metallic reflectors (PMRs) to extend radio coverage to non-line-of-sight (NLoS) areas. In particular, we perform visibility analysis to find the direct and indirect visibility regions, and using these, we derive a geometry-and-blockage-aided path loss model. We then formulate the network planning problem as two independent optimization problems, placement of gNB(s) and PMRs, to maximize the coverage area with a certain quality-of-service constraint and minimum cost. We test the efficacy of our proposed approach using a generic map and compare our simulation results with the ray-tracing solution. Our simulation results show that considering the first-order reflections in planning the mmWave network helps reduce the number of PMRs required to cover the NLoS area and the gNB placement aided with PMRs requires fewer gNBs to cover the same area, which in turn reduces the deployment cost., Comment: Under review for a possible publication in IEEE Trans. Vehicular Technology, 2020

Ground-to-air (GA) communication using unmanned aerial vehicles (UAVs) has gained popularity in recent years and is expected to be part of 5G networks and beyond. However, the GA links are susceptible to frequent blockages at millimeter wave (mmWave) frequencies. During a link blockage, the channel information cannot be obtained reliably. In this work, we provide a novel method of channel prediction during the GA link blockage at 28 GHz. In our approach, the multipath components (MPCs) along a UAV flight trajectory are arranged into independent path bins based on the minimum Euclidean distance among the channel parameters of the MPCs. After the arrangement, the channel parameters of the MPCs in individual path bins are forecasted during the blockage. An autoregressive model is used for forecasting. The results obtained from ray tracing simulations indicate a close match between the actual and the predicted mmWave channel., Comment: Now the updated manuscript has been accepted for publication in IEEE Antenna and Wireless Propagation Letters

Millimeter-wave (mmWave) communication systems require narrow beams to increase communication range. If the dominant communication direction is blocked by an obstacle, an alternative and reliable spatial communication path should be quickly identified to maintain connectivity. In this paper, we introduce a new metric to quantify the effective multipath richness (EMR) of a directional communication channel by considering the strength and spatial diversity of the resolved paths, while also taking into account beamwidth and blockage characteristics. The metric is defined as a weighted sum of the number of multipath component (MPC) clusters, where clustering is performed based on the cosine-distance between the MPCs that have power above a certain threshold. This process returns a single scalar value for a transmitter (TX)/receiver (RX) location pair in a given environment. It is also possible to represent the EMR of the whole environment with a probability distribution function of the metric by considering a set of TX/RX locations. Using this proposed metric, one can assess the scattering richness of different communication environments to achieve a particular quality of service (QoS). This metric is especially informative and useful at higher frequencies, such as mmWave and terahertz (THz), where the propagation path loss and penetration loss are high, and directional non-light-of-sight (NLOS) communication is critical for the success of the network. We evaluate the proposed metric using our channel measurements at 28 GHz in a large indoor environment at a library setting for LOS and NLOS scenarios., Comment: This work has been submitted to the IEEE for possible publication

This paper investigates the problem of classification of unmanned aerial vehicles (UAVs) from radio frequency (RF) fingerprints at the low signal-to-noise ratio (SNR) regime. We use convolutional neural networks (CNNs) trained with both RF time-series images and the spectrograms of 15 different off-the-shelf drone controller RF signals. When using time-series signal images, the CNN extracts features from the signal transient and envelope. As the SNR decreases, this approach fails dramatically because the information in the transient is lost in the noise, and the envelope is distorted heavily. In contrast to time-series representation of the RF signals, with spectrograms, it is possible to focus only on the desired frequency interval, i.e., 2.4 GHz ISM band, and filter out any other signal component outside of this band. These advantages provide a notable performance improvement over the time-series signals-based methods. To further increase the classification accuracy of the spectrogram-based CNN, we denoise the spectrogram images by truncating them to a limited spectral density interval. Creating a single model using spectrogram images of noisy signals and tuning the CNN model parameters, we achieve a classification accuracy varying from 92% to 100% for an SNR range from -10 dB to 30 dB, which significantly outperforms the existing approaches to our best knowledge., Comment: 18 pages, 9 figures, 4 tables, journal

In this paper, an analytical framework is provided to analyze the energy coverage performance of unmanned aerial vehicle (UAV) energy harvesting networks with clustered user equipments (UEs). Locations of UEs are modeled as a Poisson Cluster Process (PCP), and UAVs are assumed to be located at a certain height above the center of user clusters. Hence, user-centric UAV deployments are addressed. Two different models are considered for the line-of-sight (LOS) probability function to compare their effects on the network performance. Moreover, antennas with doughnut-shaped radiation patterns are employed at both UAVs and UEs, and the impact of practical 3D antenna radiation patterns on the network performance is also investigated. Initially, the path loss of each tier is statistically described by deriving the complementary cumulative distribution function and probability density function. Following this, association probabilities with each tier are determined, and energy coverage probability of the UAV network is characterized in terms of key system and network parameters for UAV deployments both at a single height level and more generally at multiple heights. Through numerical results, we have shown that cluster size and UAV height play crucial roles on the energy coverage performance. Furthermore, energy coverage probability is significantly affected by the antenna orientation and number of UAVs in the network.

Deployment of unmanned aerial vehicles (UAVs) is recently getting significant attention due to a variety of practical use cases, such as surveillance, data gathering, and commodity delivery. Since UAVs are powered by batteries, energy efficient communication is of paramount importance. In this paper, we investigate the problem of lifetime maximization of a UAV-assisted network in the presence of multiple sources of interference, where the UAVs are deployed to collect data from a set of wireless sensors. We demonstrate that the placement of the UAVs play a key role in prolonging the lifetime of the network since the required transmission powers of the UAVs are closely related to their locations in space. In the proposed scenario, the UAVs transmit the gathered data to a primary UAV called \textit{leader}, which is in charge of forwarding the data to the base station (BS) via a backhaul UAV network. We deploy tools from spectral graph theory to tackle the problem due to its high non-convexity. Simulation results demonstrate that our proposed method can significantly improve the lifetime of the UAV network., Comment: 5 pages, 6 figures

Future cellular systems will make use of millimeter wave (mmWave) frequency bands. Many users in these bands are located indoors, i.e., inside buildings, homes, and offices. Typical building material attenuations in these high frequency ranges are of interest for link budget calculations. In this paper, we report on a collaborative measurement campaign to find the attenuation of several typical building materials in three potential mmWave bands (28, 73, 91 GHz). Using directional antennas, we took multiple measurements at multiple locations using narrow-band and wide-band signals, and averaged out residual small-scale fading effects. Materials include clear glass, drywall (plasterboard), plywood, acoustic ceiling tile, and cinder blocks. Specific attenuations range from approximately 0.5 dB/cm for ceiling tile at 28 GHz to approximately 19 dB/cm for clear glass at 91 GHz., Comment: keywords: mm-wave; attenuation

Partially-overlapping tones (POT) are known to help mitigate co-channel interference in uncoordinated multi-carrier networks by introducing intentional frequency offsets (FOs) to the transmitted signals. In this paper, we explore the use of (POT) with reinforcement learning (RL) in dense networks where multiple links access time-frequency resources simultaneously. We propose a novel framework based on Q-learning, to obtain the (FO) for the multi-carrier waveform used for each link. In particular, we consider filtered multi-tone (FMT) systems that utilize Gaussian, root-raised-cosine (RRC), and isotropic orthogonal transform algorithm (IOTA) based prototype filters. Our simulation results show that the proposed scheme enhances the capacity of the links by at least 30\% in additive white Gaussian noise (AWGN) channel at high signal-to-noise ratio (SNR), and even more so in the presence of severe multi-path fading. For a wide range of interfering link densities, we demonstrate substantial improvements in the outage probability and multi-user efficiency facilitated by (POT), with the Gaussian filter outperforming the other two filters.

As new use cases are emerging for unmanned aerial systems (UAS), advanced wireless communications technologies and systems need to be implemented and widely tested. This requires a flexible platform for development, deployment, testing and demonstration of wireless systems with ground and aerial nodes, enabling effective 3D mobile communications and networking. In this paper, we provide a comparative overview of software-defined radios (SDRs), with a specific focus on SDR hardware and software that can be used for aerial wireless experimentation and research. We discuss SDR hardware requirements, features of available SDR hardware that can be suitable for small UAS, and power measurements carried out with a subset of these SDR hardware. We also present SDR software requirements, available open-source SDR software, and calibration/benchmarking of SDR software. As a case study, we present AERPAW: Aerial Experimentation and Research Platform for Advanced Wireless, and discuss various different experiments that can be supported in that platform using SDRs, for verification/testing of future wireless innovations, protocols, and technologies.

Base station (BS) placement in mobile networks is critical to the efficient use of resources in any communication system and one of the main factors that determines the quality of communication. Although there is ample literature on the optimum placement of BSs for sub-6 GHz bands, channel propagation characteristics, such as penetration loss, are notably different in millimeter-wave (mmWave) bands than in sub-6 GHz bands. Therefore, designated solutions are needed for mmWave systems to have reliable quality of service (QoS) assessment. This article proposes a multi-armed bandit (MAB) learning approach for the mmWave BS placement problem. The proposed solution performs viewshed analysis to identify the areas that are visible to a given BS location by considering the 3D geometry of the outdoor environments. Coverage probability, which is used as the QoS metric, is calculated using the appropriate path loss model depending on the viewshed analysis and a probabilistic blockage model and then fed to the MAB learning mechanism. The optimum BS location is then determined based on the expected reward that the candidate locations attain at the end of the training process. Unlike the optimization-based techniques, this method can capture the time-varying behavior of the channel and find the optimal BS locations that maximize long-term performance.

The world is moving towards faster data transformation with more efficient localization of a user being the preliminary requirement. This work investigates the use of a deep learning technique for wireless localization, considering both millimeter-wave (mmWave) and sub-6 GHz frequencies. The capability of learning a new neural network model makes the localization process easier and faster. In this study, a Deep Neural Network (DNN) was used to localize User Equipment (UE) in two static scenarios. We propose two different methods to train a neural network, one using channel parameters (features) and another using a channel response vector and compare their performances using preliminary computer simulations. We observe that the former approach produces high localization accuracy considering that all of the users have a fixed number of multipath components (MPCs), this method is reliant on the number of MPCs. On the other hand, the latter approach is independent of the MPCs, but it performs relatively poorly compared to the first approach., Comment: Accepted to IEEE Southeast Conference 2020

Owing to the enhanced flexibility in deployment and decreasing costs of manufacturing, the demand for unmanned aerial vehicles (UAVs) is expected to soar in the upcoming years. In this paper, we explore a UAV aided search and rescue~(SAR) operation in indoor environments, where the GPS signals might not be reliable. We consider a SAR scenario where the UAV tries to locate a victim trapped in an indoor environment by sensing the RF signals emitted from a smart device owned by the victim. To locate the victim as fast as possible, we leverage tools from reinforcement learning~(RL). Received signal strength~(RSS) at the UAV depends on the distance from the source, indoor shadowing, and fading parameters, and antenna radiation pattern of the receiver mounted on the UAV. To make our analysis more realistic, we model two indoor scenarios with different dimensions using commercial ray-tracing software. Then, the corresponding RSS values at each possible discrete UAV location are extracted and used in a Q-learning framework. Unlike the traditional location-based navigation approach that exploits GPS coordinates, our method uses the RSS to define the states and rewards of the RL algorithm. We compare the performance of the proposed method where directional and omnidirectional antennas are used. The results reveal that the use of directional antennas provides faster convergence rates than the omnidirectional antennas., Comment: Accepted in IEEE SoutheastCon 2020, Raleigh, NC

Cellular-connected unmanned aerial vehicles (UAVs) are expected to play a major role in various civilian and commercial applications in the future. While existing cellular networks can provide wireless coverage to UAV user equipment (UE), such legacy networks are optimized for ground users which makes it challenging to provide reliable connectivity to aerial UEs. To ensure reliable and effective mobility management for aerial UEs, estimating the velocity of cellular-connected UAVs carries critical importance. In this paper, we introduce an approximate probability mass function (PMF) of handover count (HOC) for different UAV velocities and different ground base station (GBS) densities. Afterward, we derive the Cramer-Rao lower bound (CRLB) for the velocity estimate of a UAV, and also provide a simple unbiased estimator for the UAV's velocity which depends on the GBS density and HOC measurement time. Our simulation results show that the accuracy of velocity estimation increases with the GBS density and HOC measurement window. Moreover, the velocity of commercially available UAVs can be estimated efficiently with reasonable accuracy., Comment: Submitted to IEEE SPAWC 2020, Atlanta, GA

The pervasiveness of the wireless cellular network can be a key enabler for the deployment of autonomous unmanned aerial vehicles (UAVs) in beyond visual line of sight scenarios without human control. However, traditional cellular networks are optimized for ground user equipment (GUE) such as smartphones which makes providing connectivity to flying UAVs very challenging. Moreover, ensuring better connectivity to a moving cellular-connected UAV is notoriously difficult due to the complex air-to-ground path loss model. In this paper, a novel mechanism is proposed to ensure robust wireless connectivity and mobility support for cellular-connected UAVs by tuning the downtilt (DT) angles of all the GBSs. By leveraging tools from reinforcement learning (RL), DT angles are dynamically adjusted by using a model-free RL algorithm. The goal is to provide efficient mobility support in the sky by maximizing the received signal quality at the UAV while also maintaining good throughput performance of the ground users. Simulation results show that the proposed RL-based mobility management (MM) technique can reduce the number of handovers while maintaining the performance goals, compared to the baseline MM scheme in which the network always keeps the DT angle fixed., Comment: Accepted in ICC Workshop 2020, Dublin, Ireland. Fixed some errors

Propagation channel characteristics are substantially different in sub-6 GHz and millimeter-wave (mmWave) bands. A typical mmWave link experiences more than an order-of-magnitude larger path loss and is more susceptible to blockages than a traditional sub-6 GHz link. Therefore, extensive measurement campaigns with efficient channel sounders are needed for a complete understanding of the mmWave propagation channel characteristics. In addition, solutions should be sought to overcome the outage problems associated with the mmWave bands and to achieve acceptable communication performance. In this paper, our 28 GHz channel sounder is introduced that can be used with horn antennas as well as phased array antennas. The two antenna types are then compared in terms of speed they switch from one direction to another and extracted multipath component (MPC) properties, such as path gain. We then propose the use of passive reflectors and an active repeater for coverage enhancement by improving the received signal strength. In particular, ECHO reflector and TURBO repeater are introduced, and measurement results using them are presented., Comment: Accepted for presentation at IEEE SouthEastCon, Mar. 2020

Millimeter-wave (mmWave) frequency bands are expected to be used for future 5G networks due to the availability of large unused spectrum. However, the attenuation at mmWave frequencies is high. To resolve this issue, the utilization of high gain antennas and beamforming mechanisms are widely investigated in the literature. In this work, we considered mmWave end-to-end propagation modeled by individual ray sources, and explored the effects of the number of rays in the model and radiation patterns of the deployed antennas on the received power. It is shown that taking the dominant two rays is sufficient to model the channel for outdoor open areas as opposed to the indoor corridor which needs five dominant rays to have a good fit for the measurement and simulation results. It is observed that the radiation pattern of the antenna affects the slope of the path loss. Multi-path components increase the received power, thus, for indoor corridor scenarios, path loss according to the link distance is smaller for lower gain antennas due to increased reception of reflected components. For an outdoor open area, the slope of the path loss is found to be very close to that of the free space., Comment: 13 pages