List of Accepted Posters

Poster Session 1 (November 10, 2021, 2:15 – 3:45 PM)

Chair: Thomas Zajkowski

Drone Corridors, UTM, Unauthorized Drone Tracking

  1. Jayasravani Mandapaka, Sabrina Muna Islam, and Kamesh Namuduri, “Vehicle-to-Vehicle Communications in Air Corridors” (University of North Texas) [PDF]
  2. Martins Ezuma, Chethan Kumar Anjinappa, Semkin Vasilii, and Ismail Guvenc, “Comparative Analysis of Radar Cross Section Based UAV Classification Techniques” (NC State University) [PDF]
  3. Chris Lewis, “UTM Development through Data Collaboration, NC Drone Economy Growth and Workforce Development” (SPOTR Industries) [PDF]
  4. Simran Singh, Mihai L. Sichitiu, Ismail Guvenc, “Design of Drone Corridor Trajectories to Minimize Ground Risk” (NC State University) [PDF]
  5. Barry Jenkins and Francois Malassenet, “Open-Source Protocol for Continuous, Cooperative 3D Map Updating and Streaming: Supporting Sensor-Based Navigation in GPS-Denied Environments” (Primal Space Systems) [PDF]
  6. Priyanka Sinha and Ismail Guvenc “Impact of 3D antenna patterns on 3D Localization of UAV” (NC State University) [PDF]
  7. Sung Joon Maeng, Md Moin Uddin Chowdhury, and Ismail Guvenc, “Base Station Antenna Uptilt Optimization for Cellular-Connected Drone Corridors” (NC State University) [PDF]

AERPAW Current Software Defined Radio, UAV, and Networking Capabilities

  1. Talha Faizur Rahman, Keith Powell, Andrew Yingst, Vuk Marojevic, “AERPAW Software Radio Platform for UAV cellular research” (Mississippi State University) [PDF]
  2. Mark Funderburk, John Kesler, and Mihail Sichitiu, “Autonomous Vehicle Development for AERPAW” (NC State University) [PDF]
  3. Magreth Mushi, Harshvardhan Joshi, Rudra Dutta, Ismail Guvenc, Mihail Sichitiu, Brian Floyd, Thomas Zajkowski, “The AERPAW Dynamic, Multi-use Backplane Network” (NC State University) [PDF]

Smart Agriculture and LoRa IoT Experiments

  1. Veera Venkata Naga Durga Santosh Kumpatla, Monisha Mallesh, Mrugen Deshmukh, “Interference Analysis for LoRa Communications Using SDRs” (NC State University) [PDF]
  2. James Reynolds, Parvez Ahmmed, Evan Williams, and Alper Bozkurt, “A Field-Deployed Sensor System for Interdisciplinary Research and Agriculture Application” (NC State University) [PDF]
  3. Ender Ozturk, Rohit Kantheti, Dongni Yang, and Amit Singh, “LoRa/LoRaWAN Experiment Support Plans for AERPAW” (NC State University) [PDF]

Poster Session 2 (November 10, 2021, 4:00 – 5:30 PM)

Chair: Brian Floyd

AERPAW Future Platform Features

  1. Kairui Du, Omkar Mujumdar, Ozgur Ozdemir, Ender Ozturk, Ismail Guvenc, Mihail L. Sichitiu, Huaiyu Dai, and Arupjyoti Bhuyan, “60 GHz Outdoor Propagation Measurements and Analysis Using Facebook Terragraph Radios” (NC State University) [PDF]
  2. Asokan Ram, Michael Barts, Thomas Hoover, Mihail Sichitiu, Rudra Dutta, Ismail Guvenc, “Aerial Experimentation Plan Using Commercial Ericsson 5G Network for AERPAW” (Wireless Research Center and NC State University) [PDF]
  3. Mike Rogers, “A Mobile Platform For Wide Area Communications” (Carolina Unmanned Vehicles) [PDF]
  4. Udita Bhattacherjee, Ender Ozturk, Ozgur Ozdemir, Ismail Guvenc, Mihail L. Sichitiu, and Huaiyu Dai, “Experimental Study of Outdoor UAV Localization and Tracking using Passive RF Sensing” (NC State University) [PDF]
  5. Talha Faizur Rahman, Keith Powell, Andrew Yingst, and Vuk Marojevic, “UAV-UE Handover Experimentation with SDRs” (Mississippi State University) [PDF]
  6. Sung Joon Maeng, Ismail Guvenc, Mihail L. Sichitiu, and Ozgur Ozdemir, “National Radio Dynamic Zones with Autonomous Aerial and Ground Spectrum Sensors” (NC State University) [PDF]

Machine Learning, Data Science, and AI for Advanced Wireless and UAV Research

  1. Debbie Mayville, Andrea Moore, and JP Lavado, “Small Cell Deployment Optimization in 5G Networks” (SAS) [PDF]
  2. Mahdi Chehimi and Walid Saad, “Quantum Drones in Quantum Communication Networks: Opportunities, Challenges, and Performance Optimization” (Virginia Tech) [PDF]
  3. Chethan Kumar Anjinappa and Ismail Guvenc, “Coverage Hole Detection for mmWave Networks: An Unsupervised Learning Approach” (NC State University) [PDF]
  4. Bharath Keshavamurthy and Nicolo Michelusi, “Multifaceted UAV Fleet Automation via Asynchronous Deep RL: From Mechanized Farming to Hybrid RANs” (Arizona State University) [PDF]
  5. Amirahmad Chapnevis, Ismail Guvenç, Laurent Njilla, and Eyuphan Bulut, “Collaborative Trajectory Optimization for Outage-aware Cellular-Enabled UAVs” (Virginia Commonwelth University) [PDF]
  6. Chengyi Qu, Rounak Singh, Alicia Esquivel Morel, Francesco Betti Sorbelli, Prasad Calyam, and Sajal K. Das, “Multi-Drone Coordination and Networking Experiments for Disaster Response” (University of Missouri) [PDF]
  7. Christina Chaccour and Walid Saad, “Aerial Communications at THz Frequencies” (Virginia Tech) [PDF]
  8. Priyanka Sinha and Ismail Guvenc, “Neural Network-based Tracking of Maneuvering Drones using Ground RF Sensor Infrastructure” (NC State University) [PDF]
  9. Eric Adams, George Papadimitriou, Ryan Tanaka, Komal Thareja, Cong Wang, Alicia Esquivel, Songjie Wang, Chengyi Qu, Eva Deelman, Michael Zink, Anirban Mandal, and Prasad Calyam, “FlyNet: A Network-Centric Platform For Edge-To-Core UAV Application Workflows” (University of Massachusetts – Amherst) [PDF]

Titles and abstracts of all the accepted posters are provided below without particular order.

Poster Abstracts

Aerial Communications at THz Frequencies

6G systems are expected to be characterized by ubiquitous 3D coverage, which can be provided by integrated space-air-ground communications. In fact, to improve the scalability of non-terrestrial networks (NTN)s, space aerial communications are envisioned to migrate towards a new class of integrated UAVs and miniaturized satellites consisting of low earth orbit (LEO) satellites and CubeSats. To cater for this increased mobility and in contrast to optical communications, THz links have considerably larger beamwidths, thus, making the beam positioning and alignment more practical. Furthermore, to meet the increased capacity needs of beyond 5G systems, while maintaining robust communication links amidst the uncertain THz channel, we propose the deployment of multi-mode unmanned aerial vehicles (UAV) and LEO satellites that can operate over heterogeneous frequency bands spanning both millimeter waves (mmWave) and THz frequency band. Particularly, to investigate the use of higher frequency bands on UAVs, we scrutinize the problem of deployment, power allocation, and bandwidth allocation of THz-operated UAVs. This problem is formally posed as an optimization problem whose goal is to minimize the total delays of the uplink and downlink transmissions between the UAV and the ground users by jointly optimizing the deployment of the UAV, the transmit power, and the bandwidth of each user. Our results show that our proposed algorithm can reduce the transmission delay by up to 59.3%, 49.8%, and 75.5% respectively compared to baseline algorithms that optimize only UAV location, bandwidth allocation, or transmit power control.

Small Cell deployment Optimization in 5G Networks

Deploying small cells requires Communications Service Providers to balance maximum coverage while minimizing implementation costs. Typical wireless deployments challenges such as signal strength, cell coverage, effective spectrum utilization, and power utilization have to also now include 5G Network implementation challenges such as clear line of sight, spectrum utilization and sharing, and beamforming limitations due to walls, trees, and weather. Advanced Analytics Machine Learning algorithms can effectively find the most optimal deployment options, leveraging feature engineering of the most critical metric specs. A Private 5G MEC on CBRS is discussed to deploy as a Testbed to evaluate deployment options and architecture designs for SA and Non-SA solutions.

Quantum Drones in Quantum Communication Networks: Opportunities, Challenges, and Performance

Optimizing the next generation of wireless technologies (6G and beyond) includes the deployment of quantum communication networks (QCNs). In general, QCNs achieve foolproof security, enhanced network capacity, and distributed quantum computing capabilities due to the inherent parallelism in quantum mechanical systems. Quantum networks rely on photonic hardware to send quantum information between distant nodes. However, photons sent over free space optical (FSO) channels and fiber optics suffer from significant losses that scale exponentially with the communication distance. FSO communications specifically require a line of sight (LoS) between the communicating parties. Thus, quantum repeaters must be incorporated in QCNs to extend the range of communication and to create FSO channels when there is no LoS between communicating parties. Moreover, entangled qubits and quantum states inside quantum memories have short lifetimes due to quantum decoherence. Thus, the information transmission, entanglement swapping, and quantum measurement operations must be performed promptly due to the pressing delay challenges. In this regard, mobile quantum repeaters represent a transformative solution that eases the implementations of QCNs and helps overcoming the different challenges facing their design. Recently, small-scale QCNs including drones, equipped with quantum optical hardware, as repeater nodes that distribute entangled photons were implemented. Such quantum drones perform quantum measurements in order to achieve entanglement swapping between distant nodes. In this regard, we investigate the utilization of drones, equipped with quantum optical hardware and serving as quantum repeaters, in QCNs. In particular, we analyze the open research opportunities, design challenges, and approaches to optimize the performance of quantum drones in QCNs. Precisely, we propose novel algorithms to organize the distribution of entangled qubits, design the motion and paths of the quantum drones, and minimize the delay in QCNs including quantum drones. The goal is to utilize quantum drones in order to serve all quantum users in QCNs with the maximum number of entangled pairs of photons under practical quantum mechanical constraints of delay and qubit quality.


Open-Source Protocol for Continuous, Cooperative 3D Map Updating and Streaming: Supporting Sensor-Based Navigation in GPS-Denied Environments

Primal Space Systems Inc. is developing 3D Tiles Nav, an open-source, sensor-agnostic software ecosystem designed to transform how 3D geospatial and 3D map data is acquired and disseminated. 3D Tiles Nav is a proposed open extension to the Open Geospatial Consortium’s 3D Tiles protocol for 3D data delivery. The 3D Tiles Nav protocol uses a new method that transforms conventional asset-centric structure of 3D geospatial data into a navigation-centric data packets that can dramatically reduce the bandwidth required to deliver this data in networked environments. Low-altitude aircraft using the common 3D Tiles Nav protocol can continuously function as “mapper” and “re-mapper” units to enable an efficient, distributed, cooperative 3D mapping capability at scale.

Multifaceted UAV Fleet Automation via Asynchronous Deep RL: From Mechanized Farming to Hybrid RANs

We envision a future in which UAV-aided hybrid networks encompass every aspect of the modern communication infrastructure: from beyond LoS connectivity & traffic offloading via UAV relays in dense urban neighborhoods to cellular/broadband augmentation for mechanized farming & ranching in rural towns. To manifest this vision, we leverage the tools available at our disposal from the A.I. toolbox — namely, optimization, statistical decision making, Dynamic Programming (DP), and Reinforcement Learning (RL) — to tackle communication request scheduling, path planning, data & energy harvesting, and other problems constituent in the implementation of 3D HetNets – now conceived to be integral to the “”Internet of Drones”” ecosystem in 6G deployment architectures. Our preliminary research on these problems revolves around adaptive communication request scheduling and 2D path planning for a single rotary-wing UAV serving as a cellular BS relay for distributed GNs (UEs, IoT devices, Q-UGVs, etc), with a formulation that incorporates a multi-scale Semi-Markov Decision Process (SMDP) structure: outer decisions on UAV radial velocities (in waiting states) and end positions (in communication states) optimize the average long-term delay-power trade-off; and consequently, inner decisions on angular velocities (in waiting states) and scheduling decisions & trajectories (via Hierarchical Competitive Swarm Optimization [HCSO] in communication states) greedily minimize instantaneous delay-power costs [1]. With a need to alleviate the associated computational complexity and to extend this structure to multifaceted autonomous UAV fleet management, our enhanced system model envelops prioritized request scheduling actions; deployment restrictions in terms of NFZ constraints imposed by the FAA and/or structural roadblocks; and drone service pit-stops for recharging & data upload/policy download. Employing an Asynchronous Advantage Actor Critic (A3C) framework — with a Deep Deterministic Policy Gradient (DDPG) training algorithm in the actor and a nested pair of neural networks trained via gradient descent in the critic — we derive the single-agent optimal waiting and communication state policies, which are then mapped to a distributed deployment of several UAVs through ‘conflict resolution’ and ‘spread maximization’ heuristics. Subsequent improvements here include 3D trajectory optimization; heterogeneous fleets with both rotary- & fixed-wing UAVs with advanced power models that consider acceleration, directional mobility nuances, and vertical lift; and a DP approach for path planning instead of HCSO for improved computational feasibility. Given the heterogeneity of radio frequency equipment available on the NSF AERPAW testbed; UAV piloting provisions; and our experience working with cloud-provisioned SDRs & compute nodes on the NSF POWDER platform at the University of Utah in Salt Lake City [2] (OAI E2E SDR-based LTE network, OTA BRS srsLTE, OTA BRS GNURadio OFDM Tx/Rx, 28GHz V2X measurement campaign [3], DARPA SC2 CRN tests [4, 5], and RENEW mMIMO experiments [6]), we are optimally positioned to start testing our multifaceted autonomous UAV fleet orchestration algorithms on AERPAW. Starting with the straightforward F2F1, F2F2, F2AM, and F2AM-PT scenarios to get a feel for the testbed’s capabilities, we plan to scale our experiments on AERPAW: first, we intend to deploy our trajectory optimization algorithms (HCSO, DP) via the F2AM-AT scenario vis-à-vis delay-power cost minimization; and next, integrate this path planning optimization with prioritized communication request scheduling (with commensurate rewards), data harvesting (from IoT-sensor/IoT-gateway PAWs), and drone service pit-stops (recharging & data upload/policy download) — in systems with multiple BSs, multiple GNs, and multiple UAVs — via the MFMM scenario. Additionally, given the research overlap, we intend to collaborate with researchers from Purdue University’s School of Electrical & Computer Engineering and College of Agriculture to augment their research on system-level coverage analyses for cellular networks with UAV data relays [7] through AERPAW implementations of mmWave network deployments for agricultural fleets (harvesters + UAV relays) and UAV-assisted data & energy harvesting for soil sensors.

[1] M. Bliss and N. Michelusi, “”Power-Constrained Trajectory Optimization for Wireless UAV Relays with Random Requests””, IEEE ICC 2020
[2] https://powderwireless.net/
[3] B. Keshavamurthy, et al., “”A Robotic Antenna Alignment and Tracking System for Millimeter Wave Propagation Modeling””, USNC-URSI NRSM 2022
[4] B. Keshavamurthy and N. Michelusi, “”Learning-based Cognitive Radio Access via Randomized Point-Based Approximate POMDPs””, IEEE ICC 2021
[5] B. Keshavamurthy and N. Michelusi, “Learning-based Spectrum Sensing and Access in Cognitive Radios via Approximate POMDPs””, IEEE TCCN 2021
[6] https://wiki.renew-wireless.org/en/home
[7] Y. Zhang et al., “”Large-Scale Cellular Coverage Analyses for UAV Data Relays via Channel Modeling””, IEEE ICC 2020″

Experimental Study of Outdoor UAV Localization and Tracking using Passive RF Sensing

Extensive use of unmanned aerial vehicles (UAVs) is expected to raise privacy and security concerns among individuals and communities. In this context, 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, a central controller performs a time difference of arrival (TDoA) based localization 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.


LoRa/LoRaWAN Experiment Support Plans for AERPAW

Internet-of-things (IoT) applications are known to have three major advantages, i.e., large scale deployment, high efficiency in terms of energy consumption, and low cost. Long Range Wide Area Networking (LoRaWAN) is one of the emerging IoT networking paradigms and it attracted much attention from both academia and industry, since it specifies an open standard and allows users to build autonomous Low Power Wide Area Networking (LPWAN) networks with no dependence on any third-party infrastructure. Many networks using LoRa technology have been developed recently, e.g., managing solar plants in Carson City, Nevada, USA and power monitoring in Lyon and Grenoble, France. The NSF AERPAW platform at NC State University will support LoRa experiments for the users of the platform at different layers. In particular, physical layer implementations will be provided with RonothTM LoStik LoRa USB dongles. These are LoRaWAN compatible end-user devices, which allow experiments on both physical and network layers. LoStiks operate on 915 MHz unlicensed ISM band and have the capability of LoRa and conventional FSK modulation on the physical layer. Dongles work on a Microchip RN2903 LoRa module and all the RN2903 module’s settings and commands are transmitted over UART using the ASCII interface. LoStiks have configurable parameters used in PHY level experiments such as center frequency, output power, spreading factor, bitrate and GFSK shaping factor. Some of these parameters are used only with LoRa modulation, and some of them with FSK only. These devices are capable to act as a node in a LoRaWAN network. To make PHY level LoRa experiments available in AERPAW, a code base is developed that utilizes several configuration files, which make the process more user-friendly. Current setup takes in configuration files to configure device parameters and transmitted packets. It also stores transmitted packets, received packets with measured signal to noise ratio (SNR) and received signal strength indicator (RSSI) values for postprocessing. LoRa dongles are installed in 2 fixed locations already, i.e., Lake Wheeler (LW1) and EBII rooftop (CC2), with a rollout plan to additional 5 fixed node locations by the end of 2022. Due to small size, mobile nodes such as UAVs and UGVs will also be able to accommodate LoRa PHY level, which will widely increase possible experiment diversity. Link layer setup is under development, and will be ready during AERPAW’s Phase-2, and sample experiments will be subsequently made available to AERPAW’s users. AERPAW plans to offer application-level experiments of LoRA technology as well to its users. This capability is provided using Laird Connectivity and Dragino Gateways, modules and several different type of sensors, e.g., temperature, humidity, photosensitive and flame. Initial results on PHY level experiments are shared in this poster.

A Mobile Platform For Wide Area Communications

Natural disasters, industrial accidents, and terrorist acts often degrade essential emergency communications systems. With widespread power outages infrastructure such as cell towers will only operate as long as their battery backups last. Local, state and federal response agencies will need to cover very large areas, in the case of Hurricanes and blizzards, hundreds of miles. Short range communications provided by mobile communications towers are inadequate for the urgent requirements. Response agencies including the National Guard need low cost, responsive, and mobile equipment for wide area resilient and durable communications. Carolina Unmanned Vehicles Lightweight Aerostat (LAS) provides this capability. LAS uses a specially designed tethered blimp, called a Helikite that combines helium and wind lift so even very small sizes operate easily in high wind, allowing it to be a fraction of the cost and manpower of traditional lighter-than-air designs. With all equipment, including winch, helium system, and generator, carried by a single HMMWV / pickup truck trailer Carrier, LAS is operated by a two person crew. LAS provides a unique rapidly deployable persistent 24 / 7 communications relay capability in a small mobile package. In 30 to 45 minutes from deployment it provides emergency managers with a 120 mile communications circle. Relaying from unit to unit LAS can cover an entire state, connecting local resources to state emergency command centers. LAS covers a far larger area than tower based relays, and has reduced blind spots due to hilly terrain or buildings. With Roll On – Roll Off Capability on National Guard C-130s it can even be deployed to areas cut off by flooding or other disasters.

Impact of 3D Antenna Patterns on 3D Localization of UAV

Next big commercial applications of drones require the drones to fly beyond the visual line of sight (BVLOS). This inevitable ability to fly BVLOS will also require higher tracking and localization accuracy, in order to ensure successful completion of the intended services. In this context, we explore the fundamental limits of the 3D localization of drones in conjunction with the effects of the 3D antenna radiation patterns. Although localization of drone/unmanned aerial vehicle (UAV) is a well studied topic in the literature, its relationship to the antenna effects remains mostly unexplored. In particular, we consider a fixed number of radio-frequency (RF) sensors equipped with a single or multiple dipole antennas, that are placed at some known locations on the ground and collect time-difference-of-arrival (TDOA) measurements from the UAV, that is also equipped with a dipole antenna. These measurements are then used to the estimate the 3D location of the UAV, and we derive the Cramer-Rao lower bound (CRLB) on the localization error for the various orientations of the dipole antennas at the transmitter and the receiver, namely: vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH), in a purely line-of-sight (LoS) environment and a mixed LoS/Non-line-of-sight (NLoS) environment. We show that the localization accuracy changes in a non-monotonic pattern with respect to the drone altitude and identify the respective critical altitudes for each of the ’VV’, ’VH’ and ’HH’ orientations. Subsequently we propose a multi-antenna signal acquisition technique that mitigate the performance loss due to the said antenna pattern mismatches. We then derive the localization CRLB pertaining to the multi-antenna scenario, in order to demonstrate the improved the robustness of the localization performance to any changes in the orientations of the antennas.

National Radio Dynamic Zones with Autonomous Aerial and Ground Spectrum Sensors

The concept of a National Radio Dynamic Zone (NRDZ) is recently being considered for forming geographically bounded areas where the transmitters and receivers within an NRDZ can operate while protecting the nearby licensed users of the spectrum. An NRDZ can help develop, test, and improve new spectrum technologies, waveforms, protocols, in a large-scale outdoor test area that may be close to typical development environments of wireless technologies. An important question regarding the feasibility of an NRDZ is the ability to predict, detect, and prevent leakage of radio emission from an NRDZ to a specific receiver (fixed or mobile), and to a specific geographical area. Of particular concern are the highly sensitive radio-astronomy passive receivers. In addition, any number of collocated (possibly passive) incumbents may have to be effectively protected by an effective set of mechanisms. In this paper, we introduce and summarize an NRDZ concept that relies on a combination of aerial and ground sensor nodes for spectrum sensing and Radio Environment Map (REM) development. With mobile aerial/ground sensors, the locations of a subset of spectrum sensors can be dynamically changed to most effectively obtain a reliable spectrum monitoring system across all locations/frequencies of interest for the NRDZ. We pursue a theoretical approach to answering these NRDZ wireless propagation questions complemented with an experimental component that uses AERPAW: Aerial Experimentation and Research Platform on Advanced Wireless.

Comparative Analysis of Radar Cross Section Based UAV Classification Techniques

The study investigates the problem of UAV identification using radar cross-section (RCS) signature measured in an anechoic chamber. The RCS of six commercial UAVs is measured at 15 GHz and 25 GHz in both the vertical-vertical polarization and the horizontal-horizontal polarizations. The RCS signatures are used to train 15 different classification algorithms, each belonging to one of three different categories: Statistical learning, machine learning, and deep learning (transfer learning). The study shows that while the classification accuracy of all the algorithms increases with the signal-to-noise ratio (SNR), the classification tree machine learning algorithm achieved superior accuracy of performance of 98.66% at a low SNR of 3 dB. This observation is investigated using Monte Carlo analysis with the aid of boxplots, confusion matrices, and classification plots. The accuracy performance of the classification tree is followed by the Peter Swerling statistical models and the discriminant analysis machine learning algorithm. In general, the machine and statistical learning algorithms outperformed the deep learning 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 statistical learning algorithms achieved good classification accuracy, the computational time was relatively long when compared to the other algorithms. Also, the study shows that the classification tree achieved the fastest average classification time of about 0.46 ms.

Vehicle-to-Vehicle Communications in Air Corridors

Air corridors are virtual highways in the sky for transportation of people and cargo in the controlled airspace at an altitude of around 1000 ft. to 2000 ft. above the ground level. Air corridors contain skylanes. A skylane is equivalent to one lane in a highway system. The design of such air corridors, traffic rules in the air corridors, safety requirements, and performance specifications are still evolving. There is a need for finding a substitute for traffic lights in air corridors. The most obvious choice for this substitution is vehicle to vehicle (V2V) communications among the Unmanned aircraft Systems (UASs). With V2V communications, UASs can share their intent with others and negotiate strategies for collision avoidance during close encounters. In this poster, we present potential use cases, operational assumptions, and strategies for V2V communications in air corridors. It is our intention to design relevant experiments on AERPAW.

Design of Drone Corridor Trajectories to Minimize Ground Risk

Unmanned Aircraft Systems (UAS) operations are being afforded great interest and support by the academia and the industry, as well as by regulatory bodies. For instance, in the United States, the Federal Aviation Agency (FAA) Part 107 allows pilots to fly Unmanned Aerial Vehicles (UAVs) over pedestrians and moving vehicles, and also at night in controlled airspace. To further ease the integration of UAS operations into the national airspace, particularly over populated regions, it is also useful to minimize the risk posed to ground users, buildings, and vehicles due to UAV flight. This risk can be represented by a numerical metric which we term the ground risk. Additionally, many UAS applications depend on the presence of a reliable wireless communication link between the UAV to a control station for the transmission of UAV position, surveillance video, UAV payload commands, and other mission-related data. Such wireless communication requirements also need to be considered in the design of UAS systems. Defining a drone corridor to be a set of non-intersecting three dimensional (3D) lanes along which UAVs fly, in this work, we study the design of trajectory of each lane in the corridor to minimize the sum of ground risk per lane, while satisfying constraints on the wireless communication outage probability along each lane, and on the integrated angular change of each lane. Imposing a constraint on the integrated angular change along a lane enables the drone corridor designer to indirectly influence the shape and total curvature of the trajectory. Its value may be tuned by considering UAV maneuverability and UAV energy consumption. To solve this optimization problem, we use constraint based search (CBS) in conjunction with A* search and evaluate this algorithm for the dense urban environment of Manhattan. Results are presented for various outage probabilities and maximum integrated angular change per lane. This research can be utilized to make UAS operations safe and reliable, and accelerate its adoption.

FlyNet: A Network-Centric Platform For Edge-To-Core UAV Application Workflows

FlyNet is a multi-discipline NSF sponsored research project that examines compute and network resource provisioning and software deployment on the edge and in the cloud, specifically with an eye toward applications for Unpiloted Aerial Vehicles (UAVs). Here we describe two candidate workflows that we have developed. The first of these is a scalable solution for dynamic flight path routing around observed and forecast weather obstacles. The workflow includes the extraction of UAS Volume Restriction (UVR) obstacles representing areas of impactful weather from real data sources, and tailored to specific tolerances of the aircraft. Subscription services provide ongoing flight path routing in the cloud as the uncertain weather forecasts evolve over time, and a flight simulator that advects each vehicle along it’s path. The second workflow simulates a drone flying through a 4G cell network mesh and collecting video data for analytics processing with a neural network algorithm. We instantiate a set of heterogeneous, shared IoT devices onto which we can offload video frames and run the object classifications. We collect real and simulated network and CPU load metrics that are fed into a utility function to determine how best to utilize available routes and candidate workers, and close the loop by using network emulation to mimic likely rates and transmitting data for processing. Results are demonstrated in terms of improved throughput and an analysis of the accuracy of the analytics suite.

60 GHz Outdoor Propagation Measurements and Analysis Using Facebook Terragraph Radios

Millimeter-wave (mmWave) bands offer a large amount of available bandwidth that can support a larger number of users with higher throughputs compared to the sub-6 GHz frequencies. However, 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 (NC State University) 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 with phased-array antennas. Both the transmitter (Tx) and the receiver (Rx) antennas scanned an elevation angle from -45 degree to 45 degree, with 1.4 degree resolution, and the HPBW of the each antenna array was 2.8 degree. Results include received power, path loss, maximum signal-to-noise ratio (SNR), and maximum root mean square (RMS) delay spread at all beamforming directions supported by the antenna array. Direct line-of-sight (LOS) propagation exists in both links. Path loss was observed to be 110.2 dB at a travel distance of 98 m for the aerial link, and was 117.05 dB at a separation of 147 m for the ground link. Our measured path loss also provided a best fit to the 3GPP and 5GCM urban canyon path loss models. A peak SNR of 18 dB and 15 dB was achieved in the LOS region of the 2 links. Maximum RMS delay was 3.88 ns and 4.83 ns, respectively. 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.

Collaborative Trajectory Optimization for Outage-aware Cellular-Enabled UAVs

Cellular-enabled unmanned aerial vehicles (UAVs) require almost continuous cellular network connectivity to fulfill their missions successfully. However, the area (e.g., rural) they fly over may have partial coverage, making the path planning of such UAV missions a challenging task. Recently a tolerable outage duration is taken into account for such UAVs, and the trajectory optimization under this outage duration is studied. However, these existing studies consider only a single UAV and focus on optimization of each UAVs own path separately even in multi-UAV scenarios. In this paper, we study the trajectory optimization problem for cellular-enabled UAVSs by taking into account the collaboration among UAVs. That is, for a given set of point, we aim to optimize the total mission completion time for all UAVs such that none of them has a connection outage more than a threshold. We let UAVs collaborate and provide connectivity as relays to each other to solve their outage problem and shorten their trajectories. We first model and solve this problem using nonlinear programming after discretization of the problem. Since it takes longer to solve the problem with an approach, we then provide a graph-based approximation solution that runs fast. Numerical results show that the proposed approximate solution provides close to optimal results and performs better than state-of-the-art solutions that consider each UAV separately without collaboration among UAV. In addition to this new novel solution, we work on other heuristic algorithms which are not tested on real world scenarios. Evaluating the proposed algorithms on AERPAW testbed can help us determine the strength and weakness of them in real environments.

Coverage Hole Detection for mmWave Networks: An Unsupervised Learning Approach

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 lowdimensional 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. Our future plans include testing the approach in this work through experiments in AERPAW.


Base Station Antenna Uptilt Optimization for Cellular-Connected Drone Corridors

The concept of drone corridors is recently getting more attention to enable connected, safe, and secure flight zones in the national airspace. To support beyond visual line of sight (BVLOS) operations of aerial vehicles in a drone corridor, cellular base stations (BSs) serve as a convenient infrastructure, since such BSs are widely deployed to provide seamless wireless coverage. However, antennas in the existing cellular networks are down-tilted to optimally serve their ground users, which results in coverage holes if they are also used to serve drones. In this poster, we consider the use of additional uptilted antennas at cellular BSs and optimize the uptilt angle to minimize outage probability for a given drone corridor. Our numerical results show how the beamwidth and the maximum drone corridor height affect the optimal value of the antenna uptilt angle.

Multi-Drone Coordination and Networking Experiments for Disaster Response

Unmanned aerial vehicles or drones provide new capabilities for disaster response management (DRM). In a DRM scenario, multiple heterogeneous drones collaboratively work together forming a flying ad-hoc network (FANET) instantiated by a ground control station. However, FANET air-to-air and air-to-ground links that serve critical application expectations can be impacted by: (i) environmental obstacles (i.e., buildings, trees, wind), and (ii) limited battery capacities. In this poster, we present a novel obstacle-aware and energy-efficient multi-drone coordination and networking scheme that features heterogeneous drone setups coupled with a packet forwarding algorithm for drone-to-ground network establishment. The main goal of the experimentation is to generate a flying ad-hoc network (FANET) for disaster response management purposes with multiple heterogeneous drones collaboratively working together. Features such as environmental-awareness and energy-awareness models are supported. Initially, we evaluated our scheme by comparing it with state-of-the-art networking algorithms in a trace-based DRM FANET simulation testbed. Results in simulation show that our scheme improves network connectivity performance while also providing significant energy savings in various simulated DRM environments. To achieve new experiments, we plan to extend the topology based on scenario 8 – Fixed to Aerial Mobile with Autonomous Trajectory (F2AM-AT) in AERPAW. The new experiments will be built by adding obstacle awareness models inside the current scenario, as well as presenting a heterogeneous drone setup to emulate a DRM scheme. More specifically, we plan to utilize our A3C trajectory prediction algorithm in terms of obstacle awareness and energy efficiency features to set up the experiment. Supported devices such as drones with various computation capaticities, wideband RF sensors, and Fortem Radar are needed. Furthermore, our experiments are reproducible and reusable in any heterogeneous drone setups. In addition to this, we plan to use the AERPAW platform to generate more traces as well as evaluate the performance of our proposed approach.

AERPAW Software Radio Platform for UAV Cellular Research

An unmanned aerial vehicle (UAV) is regarded as an enabler technology for next generation networks. It experiences radio propagation conditions different from that of in traditional cellular networks. Therefore, it is important to experimentally investigate the performance of cellular communications and networking innovations while serving aerial radios. In this work, we examine the performance of low-altitude aerial nodes that are served by an open-source software-defined radio (SDR) network. We provide a detailed description of the open-source hardware and software components needed for establishing an SDR-based broadband wireless link, and present radio performance measurements. Our results with a standard compliant software-defined 4G system show that an advanced wireless testbed for innovation in UAV communications and networking is feasible with commercial off-the shelf hardware, open-source software, and low-power signaling.

UAV-UE Handover Experimentation with SDRs

Mobility management is one of the key aspects of terrestrial networks. Due to 3D mobility of unmanned aerial vehicles (UAVs)-aided cellular networks, cell-association becomes a challenge for cellular users. This is particularly because of UAV 3D flight path which allows UAV to traverse several cells, as a result of which several handovers can occur that give rise to ping-pong effects in the network. Our research on mobility management enables design, implementation, and testing methodology for handover experiments with aerial users. We leverage software-defined radios (SDRs) and implement a series of tools for in-lab testing and for outdoor field testing. Our research experiments are based on commercial off-the-shelf hardware. open-source software, and an experimental license to enable reproducible and scalable experiments.
Our initial outdoor results with two SDR base stations connected to an open-source software core network, implementing the 4G long-term evolution protocol, and one low altitude UAV user equipment demonstrate the handover process.

Aerial Experimentation Plan Using Commercial Ericsson 5G Network for AERPAW

The NSF AERPAW platform at NC State University (NCSU) plans to support a multitude of wireless platforms and technologies for aerial experimentation and research. One such experimentation platform is a commercial grade and standard compliant 5G wireless network using state-of-the-art and advanced Ericsson infrastructure equipment. Having such a commercially deployed network allows the researchers to build experiments over and above the current system, beyond the 3GPP specifications, and run them reliably. The currently deployed Ericsson network is a 3GPP Release 15 compliant 5G NSA (non-standalone) with EN-DC Option 3 (EUTRA-NR Dual Connectivity) network architecture. This 5G network supports a NR sector in frequency band n78 with an LTE sector in frequency Band 66 as an anchor node connecting to a NSA EPC Core. The network supports two sectors, 120 degrees wide adjacent to each other, for multi-sector and handover types of experiments. The network is capable of supporting 2×2 or 4×4 MIMO, 100 MHz channel bandwidth maximum and carrier aggregation (CA) for enhanced Mobile Broadband (eMBB) or any heavy throughput centric experiments. The Ericsson network is currently deployed with RAN at the Lake Wheeler site and Core at the NCSU Campus, and it is being tested with various commercial 5G UE devices, including iPhone 12 Pro and Motorola Edge+. Next, the plan is to integrate various UAVs (built by AERAW team at NCSU or commercially available), associated services and solution UAV platforms to enable the Ericsson network for aerial experimentations. One of the goals is to integrate Ericsson Iris UAV software platform to build autonomous drone applications and solutions.

Interference Analysis for LoRa Communications Using SDRs

The emergence of Internet of Things (IoT) has called for contemporary communication alternatives that consist of substantial number of sensors as well as smart devices. Unlike traditional cellular communication technologies, IoT links have a very low data rate, and they can operate for a long period over the air. For these IoT sensors, lower power consumption is a prerequisite to transmit data over long ranges to increase battery lifetime. In order to meet power specifications, a Low-Power Wide Area Network (LP-WAN) technology, LoRa, has been gaining major traction for various IoT use cases. LoRa is an efficient technology to increase the communication range in industrial, scientific and medical (ISM) bands in the United States. The AERPAW platform at North Carolina State University supports experiments over Universal Software Radio Peripheral (USRPs) in realistic deployment scenarios. One of the future goals is to carry out LoRa IoT experiments using USRPs, LoRa USB dongles, and commercial LoRa gateways/sensors. In this poster, we will present results using an open source LoRa PHY implementation on the AERPAW platform [1]. Operating under interference is an important challenge in densely deployed LoRa networks, to enable reliable communication over long range. In our preliminary results, successful transmission of LoRa packets from one USRP to another USRP is to be verified under the influence of LoStik LoRa USB dongle, which acts as the interferer for the transmission. Our project aims to make LoRa USB dongle interface with USRP and analyze the effect of interferer signal on the received signal for different LoRa spreading factors [2], and the ways to mitigate such effects. Experiments are being carried out using two USRP B210 and a LoRa USB dongle, and results are being analyzed to meet the goals. We will present some preliminary results as part of this poster presentation.

References:
[1] https://github.com/tapparelj/gr-lora_sdr
[2] O. Mujumdar, H. Celebi, I. Guvenc, M. Sichitiu, S. Hwang and K. -M. Kang, “”Use of LoRa for UAV Remote ID with Multi-User Interference and Different Spreading Factors,”” 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 2021, pp. 1-7, doi: 10.1109/VTC2021-Spring51267.2021.9448804

The AERPAW Dynamic, Multi-use Backplane Network

AERPAW requires a multitude of network functions for facility management and Experimenter support over a variety of equipment ranging from programmable Linux substrates to commercial “black-box” equipment installed in the field at locations ranging over several square miles, with dynamically changing connectivity and topology equipment. Unique challenges arise because of the juxtaposition of these requirements with constraints posed by capex and opex costs, as well as the need to co-exist within the much more staid network infrastructure of NC State University’s physical plant, and extending that physical plan and backbone network without requiring any major architectural or operational changes to it. In this poster, we present the innovative and flexible design we have adopted to meet all requirements while navigating within the constraints.


A Field-Deployed Sensor System for Interdisciplinary Research and Agriculture Applications

The Internet of Things (IoT) community and ecosystem have paved the way for the deployment of sensors and systems in large quantities and in many different locations. Researchers in agricultural and environmental fields have a limited number of commercial options to serve as platforms for data collection. Interest in bringing IoT to agriculture has increased recently, but the hardware and software implementations are generally poorly documented. The sensors used are either commercial, which currently adds a disproportionate cost, or hobbyist, which requires special attention towards long term use and proper calibration for scientific use. We have developed a system that can obtain data from a variety of sensors and wirelessly integrate into a larger network of devices while doing so at a reduced cost. This versatile system has been used for tracking aquatic species as well as measuring moth populations and has a potential for many more interdisciplinary applications.

UTM Development through Data Collaboration, NC Drone Economy Growth and Workforce Development

This poster will cover our activities related to Unmanned Aerial Systems (UAS) and Unmanned Traffic Management (UTM) related activities at SPOTR Industries. As an example, our team of drone pilots is currently working with NC State’s Facilities Division to provide aerial UAS data for their roofing department to keep their guys safe and on the ground rather than traversing slippery rooftops. SPOTR will give access to NASA’s UTM System through Airspace Regulatory Automation software. This software will make airways safer for both manned and unmanned aircraft by increasing communication during complex disaster relief operations. SPOTR has partnered with the United States Veterans Corps (USVC) a local veteran focused nonprofit to create and implement a course to train military and first responder veterans to conduct commercial UAS operations so they can find gainful employment in the commercial drone industry. Further details and possible connections to AERPAW will be discussed during the poster presentation.

Autonomous Vehicle Development for AERPAW

Several autonomous vehicle platforms have been developed internally for use as mobile nodes in the AERPAW testbed. These include two Multirotor aircraft with a 16 & 3 kg max payload, and a rover that can carry at least 20 kg. Software systems have also been built to allow experimenters to safely control these mobile nodes during experiments. Building these custom systems affords much more control, integration, and better performance for AERPAWs needs than COTS solutions.

Neural Network-based Tracking of Maneuvering Drones using Ground RF Sensor Infrastructure

Tracking of a drone requires not only instantaneous estimates of the drone’s location, but also additional information on the drone’s mobility dynamics. Depending on the context, a drone may or may not cooperate in the tracking process, either because the drone’s communication system might not be designed for it, or it may be an unauthorized, non-cooperating, and/or a malicious UAV, trying to evade the eventual capture. Due to their small sizes and mechanical design, many of the modern UAVs, such as multirotors and quadcoptors are capable of aggressive maneuvers, which makes accurate and timely tracking of such UAVs extremely difficult. Most of the existing work on tracking of maneuvering targets in the absence of known target dynamics model rely on the applicability of a certain proposed mathematical models as an approximation of the possible 2D and 3D maneuver models as well as coordinate-uncoupled generic models for point target dynamics. However such premeditated dynamic models might not be adequate in the case of an aggressively maneuvering UAV. In this context we propose a neural network based tracking algorithm, that learns the underlying dynamic model and guidance logic of the maneuvering target by following a finite time trajectory of its 3D positions, and then predicts the next 3D position of the target based on the current position and the state evolution model learned by the neural network. These predictions are then refined using the current measurements.