HYPER-SPECTRAL COMMUNICATIONS AND NETWORKING FOR ATM: RESULTS AND PROSPECTIVE FUTURE

David W. Matolak, University of South Carolina, Columbia, SC Ismail Guvenc, North Carolina State University, Raleigh, NC

Hani Mehrpouyan, Boise State University, Boise, IDGreg Carr, Architecture Technology Corporation, Campbell, CA

Abstract

Over the past two years we have worked on a project for NASA’s Aeronautics Research Mission Directorate (ARMD) University Leadership Initiative (ULI) program. Our project is entitled Hyper-Spectral Communications, Networking and ATM as Foundation for Safe and Efficient Future Flight: Transcending Aviation Operational Limitations with Diverse and Secure Multi-Band, Multi-Mode, and mmWave Wireless links. For brevity we abbreviate this title HSCNA. The four-institution HSCNA project is the only ULI program to address communications and networking, and thus far has been extremely productive: we have published 10 journal papers, 54 conference papers, 2 book chapters, and multiple technical reports, with another 10-20 papers in review, and 2 patent applications. In addition to publications we are developing a dual-band radio system for flight testing in the 2020 Boeing Eco-Demonstrator program, have developed systems for assessing wideband short-range millimeter wave (mmWave) airport radio links, and systems for detection of unauthorized unmanned aircraft systems (UAS). We have also developed a future Concept of Operations (ConOps) document and are developing a simulation tool to assess gains of our HSCNA technologies when used in the National Airspace System (NAS). In this paper we summarize our project and provide example results and findings. We first provide a short overview of the ULI program and its goals within the ARMD Strategic Implementation Plan. We then describe our project’s six primary tasks, which are specifically, (i) the ConOps development; (ii) a comprehensive categorization and evaluation of current and planned communications technologies that can be used for aviation, across frequency spectrum spanning five orders of magnitude (e.g., 3 MHz HF through 100 GHz), including evaluation of performance gaps; (iii) design, development, and proof-of-concept testing of a multi-band aviation communication system; (iv) evaluation of

mmWave frequency bands and technologies for use in advanced airport communication applications; (v) evaluation of RF detection of unauthorized UAS via several techniques; and, (vi) development of a simulation system to enable exploration of potential gains of these HSCNA technologies in ATM. The example results we provide include analyses, computer simulations, laboratory experiments, and field testing. We also describe plans for the final phase of our project, and discuss impacts and future work.

Introduction

The National Aeronautics and Space Administration (NASA) is investigating new technologies and techniques for future safe and efficient National Airspace System (NAS) operation. Our NASA University Leadership Initiative (ULI) [1] project is addressing multiple communications and networking technologies with these broad aims.

Our ULI project is entitled “Hyper-Spectral Communications, Networking & ATM as Foundation for Safe and Efficient Future Flight: Transcending Aviation Operational Limitations with Diverse and Secure Multi-Band, Multi-Mode, and mmWave Wireless Links,” which we abbreviate HSCNA. Our HSCNA project is researching new aviation communications techniques to improve air traffic management (ATM) [2]-[4]. The Aeronautics Research Mission Directorate (ARMD) of NASA created a Strategic Implementation Plan (SIP) [5], in which they note that the large number and diversity of current aviation communication systems likely do not possess the reliability or security required for the future NAS. Aviation growth will make the NAS more dense and more complex than ever, with both piloted and unmanned aircraft systems (UAS). Our reference to aviation includes navigation and surveillance systems as well as communications—used for aircraft aloft and at airports

Our ULI team led by the University of South Carolina, with partners North Carolina State University,

This work was supported by the National Aeronautics and Space Administration under Federal Award ID number NNX17AJ94A.

Boise State University, and Architecture Technology Corporation, has been conducting research across a range of aviation communications areas and ATM. This paper describes this work, after 2.33 years. The remainder of this paper is organized as follows: in Section II we describe the overall HSCNA project and its six tasks. Section III provides some detail on each of the individual tasks, including example recent results. In Section IV we briefly describe next steps, and we conclude in Section V.

ULI and the HSCNA ProjectNASA’s ULI program, within ARMD’s

Transformative Aeronautics Concept Program, aims to “cultivate multi-disciplinary, revolutionary concepts to enable aviation transformation and harness convergence in aeronautics and non-aeronautics technologies to create new opportunities in aviation” [5]. The ARMD SIP [6] describes overall Strategic Thrusts (STs), and each ULI project must focus on one ST. Our HSCNA project focuses on ST 1: Safe, Efficient Growth in Global Operations. Also affected by our HSCNA project are two additional STs, Strategic Thrust 5: Real-Time System-Wide Safety Assurance; and Strategic Thrust 6: Assured Autonomy for Aviation Transformation.

The HSCNA project goals are to contribute to ST1 by design and evaluation of novel communication techniques at several layers of the communications protocol stack: the physical (PHY), data link layer (DLL) and networking layer. We have investigated these techniques via analytical, simulation, and measurement tools, and these will be used to assess gains to ATM capacity, efficiency, and resilience.

Our project tasks describe the actual work efforts. Throughout the 2.33 years we have worked on Tasks 1-5, with Task 6 just beginning in this third year. The six tasks are listed next, with task leader last names indicated in parentheses:

Task 1 (Carr): development of multi-band networking Concept of Operations (ConOps) for multiple phases of flight and all communication link types and modes, e.g., air-ground (AG), air-air (AA), air-“anything” (air-X, or AX), etc. This task is complete.

Task 2 (Matolak): quantification of capacity/coverage/performance of existing aviation (plus adjacent) frequency bands and technologies. Quantification of shortcomings and mid- to far-term (~2035) improvements, and assessment of growth potential. This task is complete.

Task 3 (Matolak): development of analysis/simulation software toolboxes and prototypes to assess adaptive link and network performance over

multiple frequency bands with multiple communication modes in a hyper-spectral network.

Task 4 (Mehrpouyan): quantification of capacity/efficiency gains of mmWave wireless airport subnetworks. Measurement and modeling of example channels and antennas, and validation of prototype mmWave systems in example airport network operations.

Task 5 (Guvenc): development of novel jammer and unauthorized UAS detection/localization techniques to detect and track any unauthorized UAS or jammer that enters any restricted zone.

Task 6 (Carr): development of a realistic and comprehensive ATM simulation capability to assess gains of multi-band/multi-mode and mmWave networking in terms of data link performance per aircraft, supportable traffic density, multi-vehicle collaboration, and operational benefits.

HSCNA Tasks 1-6In this section we describe the work within Tasks 1-6

in more detail. This includes example results and future work.

Task 1: Concept of Operations (ConOps)The purpose of the HSCNA ConOps is to provide the

operational context for the research, and to provide a basis for the Simulation Assessment of the proposed hyper-spectral technologies in Task 6. In our Final ConOps, completed in February 2019, we presented an overview of the HSCNA research project and its constituent tasks. We then reviewed and summarized prior NASA-funded studies related to CNS and ATM. We surveyed ongoing NASA ATM research including new projects such as UTM and ATM-X, which are being performed under the Airspace and Operations Safety Program (AOSP). We also surveyed FAA documents regarding future NAS operations. We provided a description of a future baseline NAS (NextGen NAS) based primarily on the FAA’s document entitled “The Future of the NAS.” The Future of the NAS includes narrative descriptions of capabilities that have been implemented, and those that need to be developed and implemented by 2025 to transform NAS operations and provide desired benefits to the user community. We highlighted the systems, capabilities, and the operational transformations that will provide a basis for modeling the future NextGen NAS in the Simulation Assessment.

We also provided an initial description of a Hyper-Spectral NAS that will be used to support the evaluation of the impacts of the hyper-spectral technologies compared with the NextGen NAS in the Simulation Assessment. We assume that at a minimum the Hyper-Spectral NAS includes all of the capabilities and benefits

of the NextGen NAS as described in the FAA’s “The Future of the NAS.” The Hyper-Spectral NAS will also include novel communications and networking technologies and associated changes in air traffic management capabilities beyond NextGen. For this description of the Hyper-Spectral NAS we provided a summary of the main hyper-spectral technologies and their potential operational transformations in the NAS. We reviewed the relevant Strategic Thrust Roadmaps in NASA’s SIP and identified the future NAS capabilities potentially supported by the proposed hyper-spectral technologies. We selected several ATM operational concepts defined by NASA in their Strategic Thrust 6 Roadmap to provide illustrative examples of how HSCN research in Tasks 3-5 support future ATM concepts.

Task 2: Aviation Communications and Networking Assessment

In Task 2 we compiled detailed descriptions of communication, navigation, and surveillance (CNS) systems and frequency bands, and identified potential new aviation communication solutions. We also quantified frequency spectrum use from an information theoretic perspective. This evaluation assessed existing/planned systems from aviation, satellite systems, as well as systems from commercial, military, and other domains. The Task 2 final report should aid researchers in quantifying the current state of the art, and hence determine opportunities and boundaries for future aviation communications and networking.

In this task, we grouped CNS systems according to the specific band they occupy, HF/VHF bands, L-band, C-band, and separately, satellite communication systems. We also covered existing (and coming 5G) cellular networking communication systems, local area networks, and new internet of things (IoT) systems. For the existing systems, we tabulated 14 or more specifications, several of which include the following1. RF carrier frequency range of operation.2. Overall or per-channel bandwidth.3. Modulation and multiplexing/duplexing schemes.4. Capacity/number of channels, and link spectral efficiency.

5. Link propagation mode and/or application environment (e.g., LoS, beyond LoS (B-LoS), oceanic, airport surface, etc.)6. Availability, the fraction of time the system can be reliably used.7. Typical total latency.8. Network topology, e.g., point-to-point, point-to-multipoint, relaying, broadcast, multicast, etc.

Table 1 shows a part of just one example from the report: a few communication technologies for C-band (4-8 GHz). Among these is a new system we have designed and analyzed that can improve upon, or augment the AEROMACS standard.

The Task 2 report also described several novel technologies that could be of future use in aviation communications. This includes free-space optical links, near-vertical incidence skywave for regional HF links, orbital angular momentum transmission for high-capacity short-range links, and the use of machine learning for a variety of applications such as dynamic spectrum allocation and spatial trajectory planning, as well as cognitive radio adaptation.

A few of the concluding recommendations from the final Task 2 report are as follows:1. The civil aviation community should forge a stronger level of collaboration with the cellular and military communications communities, to make use of their vast knowledge and experience in modern, reliable communication system design. 2. New, proven waveform designs and techniques (e.g., MIMO) should be considered for introduction into aviation communication links.3. Free space optical communications for aviation has enormous potential. More should be invested in this area for future aviation communications.

4. One thing that ALL surveyed technologies (except the military systems) lack is any provision for physical layer security. None of the current aviation technologies can withstand even modest jamming, nor are they anti-spoof. Hence

much greater attention to physical layer security of future aviation communication systems is recommended.

Table 1. Example C-band communication systems.WiMAX AeroMACS Proposed FBMC

for Airport (based on AeroMACS

configurations)RF freq. (MHz) 5800 5091-5150 5091-5150

Channel bandwidth

1.25, 5, 10 & 20 MHz

Varies: 1.25-20 MHz Varies: 1.25-20 MHz

Modulation schemes

CP-OFDM (B/QPSK, 16 QAM,

64 QAM)

CP-OFDM (QPSK, 16 QAM, 64 QAM)

FBMC (QPSK, 16 QAM, 64 QAM)

User data rate Up to 70 kbps Up to 25 Mbps Up to 29 MbpsCapacity 5, 8.75, and 10 MHz

channelsMultiple 10, 20 up to 100

MHz channelsMultiple 10, 20 up

to 100 MHz channels

Spectral efficiency (bps/Hz)

3.7 0.5-4.5 2.65-5.66

Duplexing/Multiple-Access

TDD/FDD TDD TDD

Typical communication

range (km)

<50 9.26 9.26

Link mode LOS/NLOS LOS/NLOS LOS/NLOSLatency (s) N/A <0.2 <0.2

Network overall

topology

Metropolitan area network (MAN)

Cellular Cellular

Main functions Portable mobile broadband

connectivity across cities &

countries.

Airport surface high data rate & ATC, ATM & AOC communications. All-IP

network system, designed to support mobile speeds

≤370 km/h.

Same as AEROMACS

Task 3: Multi-band & Multi-mode Communications & Networking

The idea behind multi-band communications is to simultaneously and/or alternately employ distinct spectral bands to achieve successful message transfer. By doing so, one can take advantage of the different channel, interference, and throughput characteristics of the different bands to increase reliability and/or capacity. The term hyper-spectral means the use of a wide spectrum, which in our case includes bands from HF to

VHF to L-band, C-band, K-bands, mmWave bands, and higher.

Multi-mode communications here means communication among aviation and (traditionally) non-aviation entities such as other vehicles (trains, ships, etc.), radio broadcast base stations, etc. This form of communication is envisioned as secondary to traditional aeronautical modes, and is advocated for future study.

In the multi-band area, we have been investigating improved waveform designs for several aviation bands.

This has led to several schemes using non-linear chirps, filterbank multicarrier (FBMC), and discrete Fourier Transform spread orthogonal frequency division multiplexing (DFT-s-OFDM). These have been evaluated analytically and in computer simulations using our measurement-based air-ground channel models [7]-[9]. We are also conducting laboratory and small-scale field tests with our designs implemented in software defined radios (SDRs).

The largest effort within Task 3 is preparation for participation in Boeing’s 2020 Eco-Demonstrator flight tests. For this, we are developing a dual-band radio system to operate in the L- and C-bands. Figure 1 shows a diagram of the aircraft radio components; the ground station (GS) system is essentially identical. Figure 2 shows a photograph of a portion of the actual RF components from our lab.

Figure 1. Dual-band radio design for Boeing Eco-Demonstrator flight testing.

Figure 2. Photograph of RF components for dual-band radios.

Several constraints have required us to use a time-division duplexing (TDD) approach, with alternate use of the L-band and C-band—we note that TDD may not be the final selected duplexing mode if our schemes become operational or standardized. For L-band, we plan to transfer CPDLC messages as well as our own data within a 500 kHz channel, using both OFDM (similar to LDACS1) and FBMC. Our FBMC design achieves larger throughput, is more robust to Doppler via the use of a smaller number of subcarriers, and is also more robust to adjacent channel DME interference, as shown in experimental results of Figure 3. For C-band, we plan to use a 5 MHz channel, and again compare OFDM to our FBMC designs using both random data and images. An example simulation result over our empirical air-ground channel models (e.g., [7], [8]) appears in Figure 4.

Our last example results on the new waveforms pertains to non-linear chirp designs. These are constant-envelope digital frequency modulations with robustness to narrowband interference and multipath [10], [11]. Figure 5 shows example chirp signal time-frequency trajectories, and Figure 6 shows our scheme performance in comparison to conventional linear chirps in the practical case of imperfect synchronization. The cross-correlations in Figure 6 are a measure of intra-system other-signal interference. As can be seen, our new quartic chirp designs outperform the traditional linear chirps in these practical conditions.

Figure 3. Bit error ratio (BER) vs. desired-signal-to-DME power ratio, laboratory experiments.

The next steps for Task 3 are to continue development of the dual-band radio designs in preparation for the Eco-Demo flight tests. This also entails refinement of a new data link layer protocol we have developed. Task 3 work will also include additional analytical, simulation, and experimental work for the multi-band designs.

Figure 4. Image transmission simulation results at two different signal-to-noise ratios (Eb/N0) showing

received images and QPSK constellations.

Figure 5. Linear and non-linear chirp waveform time-frequency traces.

(a)

(b)Figure 6. Normalized average cross-correlation vs.

delay (asynchronism) (a), and BER vs. SNR performance for multiple chirp signal sets over

empirical air-ground channels.

Task 4: mmWave Channels, Antennas, and NetworkingThe focus of Task 4 is millimeter wave (mmWave)

systems for airports and short-range aviation communications. Activities within this task include measuring and modeling various mmWave channels (e.g., at airports), designing new reconfigurable and smart mmWave antennas, evaluating coverage improvements via use of passive reflectors and active repeaters at mmWave frequencies, and quantifying mmWave wireless airport subnetwork capacity/efficiency gains. We are also investigating resource allocation algorithms, including use of machine learning techniques.

For this mmWave work, the three university teams are investigating different bands: USC is measuring at 30 GHz and 90 GHz; NCSU is measuring at 28 and 39 GHz (140 GHz in the future); and BSU is measuring at 60, 73, and 81 GHz. Channel sounders for these bands have been developed and have been used to conduct initial measurements. In Figure 7, we show some measurement results from [12] for an indoor environment—the maintenance hangar at the small municipal airport CUB in Columbia, SC. Measurements included both line-of-sight (LOS) and non-LOS (NLOS) conditions.

(a)

(b)Figure 7. Photograph of CUB airport maintenance

hangar (a) and path loss (dB) vs. link distance (m) for 5 GHz and 30 GHz.

Figure 8 shows the channels sounder platform at NC State that supports measurements at 28 GHz and 39 GHz, using horn antennas and phased arrays. The sounder hardware includes an NI PXIe-1085 TX/RX chassis, 28 GHz TX/RX mmWave radio heads from NI, FS725 Rubidium (Rb) clocks, FLIR PTU-D48E gimbals, and a rubidium (Rb) clock connected to PXIe 6674T timing modules at the TX and the RX.

To test the link quality of 28 GHz mmWave signals in an indoor environment, we carried out measurements with both the rotational directional antenna (RDA) as well as the phased array, transmitting at different angles [13]. Figure 9(a) shows the indoor measurement environment, and Figures 9(b) and 9(c) illustrate the CDF of the path gain for the RDA and the phased array, respectively, at various different azimuth AoA and AoD angles. Results show that, as expected, the path gain is maximized when the boresight of the transmit and receive beams are aligned with each other (AoA and AoD are both zero degrees), while the phased array is observed to provide slightly better link quality compared to the RDA.

Figure 8. 28/39 GHz channel sounder at NC State with horn antennas and gimbals [13].

Figure 10 shows the experimentation scenario where we use an active repeater to improve the coverage of 28 GHz indoor signals [13]. In particular, the receiver is positioned at a location which is non-line-of-sight to the transmitter, and an active repeater (TURBO from Metawave, Inc.) is placed at the corner that sees both the transmitter and the receiver, as shown in Figure 10. Results in Table 2 show that significant gains can be observed in the received signal, which diminishes with

the distance between the receiver and the TURBO repeater. Additional results with passive metallic and meta-surface reflectors are also reported in [13], which prove to be low complexity alternatives for improving indoor and outdoor wireless coverage at millimeter wave frequencies.

(a)

(b)

(c)Figure 9. Indoor 28 GHz measurements with horn

antennas and phased arrays: (a) measurement setup in a seminar hall at NCSU Engineering Building II; (b) rotating directional antenna (RDA) CDFs; (c)

phased array antenna CDFs.

Figure 10. Measurement scenario with TURBO.Table 2. Measurement results with TURBO.

In terms of antenna design, we have designed a variety of antennas for the mmWave band that provide significantly higher gains than existing antennas, while supporting more flexible beamforming capabilities compared to smart and reconfigurable antennas reported to date.

Our latest work on reconfigurable antenna design for the 23.5 to 37.7 GHz band is depicted in Figure 11. Here,

we have designed a new wideband beam steering antenna at the Ka-band for mmWave applications. The antenna configuration consists of a thin layer of a cylindrical dielectric lens partially inserted between two parallel conducting plates. The antenna is composed of a compact feed network integrated into the parallel plates and excited by an array of nine 2.92 mm coaxial connectors. Compared to previous work, the proposed design is significantly less costly to develop and due to its simple feed network structure is easy to manufacture. Moreover, as shown in Figure 12, the antenna has a 3.1 dB maximum gain variation over the entire bandwidth, which implies effective and stable radiation characteristics. Further, as shown in Figure 12, the antenna obtains a beam scanning angle of ±32° in the azimuth plane, in which the gain variation is less than 0.35 dB, and the sidelobe levels are lower than -13.2 dB over the entire bandwidth.

Figure 11. Antenna configuration and fabrication.

Figure 12. Simulated gain radiation patterns in the azimuth plane.

In terms of networking, we have been focusing on means to enable simulation of highly dense wireless networks. Densification of wireless networks is one of the primary means of supporting the expected cellular traffic growth [14]. In the development and standardization of such large networks, there is a need to have effective simulation tools to implement and design new algorithms and protocols. These simulations enable evaluations that are essential in network-layer

performance issues such as scheduling, mobility management, interference management, and cell planning [15].

There are several open-source simulators developed for different purposes in studying wireless networks. In this category, with the focus on open-source platforms, we can name Network Simulators (NS-2, NS-3) [16], [17], OMNET++ [18], J-Sim [19], and SHOX [20] platforms. These common simulators focus on preparing a platform for design and evaluation of communication protocols at the network layer or above. The physical layer modules in these platforms are not appropriate for mmWave or directional communications.

More importantly, wireless networks and more specifically cellular networks are heterogeneous. This means elements of the network can vary from large base stations to small base stations and mobile users. Further, there are certain physical mmWave phenomena that need to be considered in system-level simulations, such as blockages. In addition, mmWave directional communication changes the complexity of location-dependent searches. Specifically, search queries must take place not just in range but also in azimuth and elevation. Therefore, accurate simulation of such dense networks requires the use of spatial indexing that supports these features.

To address these issues, as shown in Figure 13, we introduce a multi-level inheritance-based architecture that is used to index all elements of a dense network on a single geometry tree. Then, we define spatial queries to accelerate searches in range, azimuth, and elevation. We demonstrate that spatial indexing can accelerate location-based searches in dense wireless networks by up to 3 orders of magnitude. Further, we have released the proposed design in an open source platform to be used by researchers in the community. In Figure 13, location and geometry properties of elements of the network are abstracted in the node object. The container stores the elements of the network on a geometry tree. From the view of the geometry tree, all elements are the same and represented as polygons. The network represents any wireless network, which can be a sensor network, mmWave network, or an integrated access and backhaul network.

Figure 13. Proposed multi-level architecture.

Future work in Task 4 includes more measurements—in all three mmWave bands—at several local airports in Boise, ID, Raleigh, NC, and Columbia, SC. Once basic measurement results are obtained, and procedures refined, we plan to conduct measurements in all three bands simultaneously at one or two major airports. Additional work on airport network management and adaptation will also continue.

Task 5: Unauthorized UAS DetectionIn this task we are working on detection, localization,

and tracking of unauthorized UAS. The aim is to develop novel unauthorized UAS detection/localization techniques to detect/track any unauthorized UAS that enters any restricted zone. Major recent activities include detection/classification of drone signals based on their RF signatures at 2.4 GHz, as well as characterization of radar cross section (RCS) signatures of various drones at 15 GHz and 25 GHz radar frequencies, for improving radar-based drone detection/classification in those bands.

Our results related to RF-based drone detection/classification are presented in [21] and some representative results are presented in Figure 14. In particular, Figure 14 shows the confusion matrix of the k-nearest-neighbor (kNN) classifier (results for several other classifiers are presented in [21]) with 17 remote controllers. On the vertical axis, the output class or the prediction of the classifier is presented, while the horizontal axis is the target class or true label. We intentionally included two pairs of identical DJI controllers (DJI Matrice 600 MPact, DJI Matrice 600, Ngat, DJI Phantom 4 Pro Mpact, and DJI Phantom 4 Pro Ngat) in our data set. Consequently, there are some confusions among these four controllers leading to a slight reduction in the classification accuracy. On the other hand, kNN still

achieves an average accuracy of 95.53%, which shows that it is robust in identifying UAV controllers of the same make and model.

Figure 14. Confusion matrix of kNN classifier, where the colorbar shows the degree of confusion in terms of

the confusion probability ρ [21].In Fig. 15, we provide RCS signatures of three

different drones at 15 GHz and 25 GHz [22]. The results show that the RCS signatures are unique to each drone, and they are relatively small, which is primarily due to the low reflective materials (plastic and carbon fiber) used in the design of these small drones. Moreover, the RCS of the Trimble zx5 drone, a relatively bigger drone, is larger than the RCS of the other drones.

Figure 15. Measured RCS (dBsm) versus azimuth angles (φ ∈ [0°, 360°]) for the small drones: (a)

Trimble zx5, (b) DJI Inspire 1, (c) DJI Phantom 4 Pro [22].

Figure 16. Receiver operating characteristics of a drone sensing network for different drone altitudes

and urban/suburban scenarios, considering a Poisson field of interferers.

Finally, Figure 16 shows the receiver operating characteristics of a ground network that senses the presence of a drone [23]. We consider this network to be an existing network (e.g. a cellular network), and hence, interference from other active users in the network are also taken into account. We observe that the drones at higher altitudes have lower probability of detection, and detection probability is significantly degraded in urban environments when compared with suburban environments. Task 6: ATM Simulations

As noted in our 2017 DASC paper [24], Architecture Technology Corporation’s Probabilistic NAS Platform (PNP) provides an appropriate level of fidelity and features, is easy to operate, and will be used for conducting the Simulation Assessment in Task 6. We will use PNP’s existing capabilities in conjunction with external data models to evaluate future NAS concepts and technologies related to hyper-spectral communications. This approach provides us with the flexibility to easily modify the external data models in order to represent a broad range of future NAS capabilities. We will take advantage of PNP’s core capabilities to analyze and visualize future NAS metrics related to both air traffic operations and network communication loading. In particular, we will perform post-processing of PNP simulation data to estimate demand for network capacities based on future NAS concepts and technologies.

We build on the prior work done by Honeywell for NASA in a study of future aeronautical communications technologies [25]. In this study Honeywell defined five types of communication services that enable ATM operations in the NAS: Air Traffic Services (ATS), Aeronautical Operations Control (AOC), Airline Administrative Communications (AAC), FAA System Wide Information Management (SWIM) communications, and Aeronautical Passenger Communications (APC). Honeywell then estimated data traffic loads for each communication service for four different categories of aircraft types: Commercial Large Aircraft, Microjets, Business and General Aviation Aircraft, and UAVs (or, UAS).

We developed a software tool called the HSCNA Data Traffic Estimator (DTE) to specify aircraft categories and communication services and associated data traffic estimates. The DTE allows a user to represent different future NAS operational concepts and capabilities by varying the values in a table of Data Rates and Communication Services—see Fig. 17.

For example, a future concept such as full Trajectory Based Operations may require significant data traffic between the air and the ground compared with a non-TBO concept. The DTE will be used in conjunction with additional modeling parameters in the PNP simulation such as airport and airspace capacity values to represent future NAS operational concepts and technologies.

Figure 17. DTE user interface example. The DTE allows a user to specify per-aircraft data rates for five

communication service types and four aircraft categories.

During our Second Annual ULI Project Review held in September 2019 in Boise, Idaho, we demonstrated an initial implementation of the DTE. For our testing and demonstration, we used future traffic schedules provided by the FAA that represent a projected day in the NAS based on historical traffic data. PNP is used to generate trajectory data based on the future traffic schedules, and then the DTE processes the trajectory data to calculate metrics related to communication services.

Using PNP trajectory data in conjunction with the DTE allows a user to calculate data usage both temporally and spatially. Figure 18 shows a time history plot of NAS-wide data usage for each Communication Service type. The DTE calculates the NAS-wide data usage based on the values specified in the DTE user interface for each aircraft flown in the data file. The graph allows interactive filtering of the data displayed so that the user can choose to display only one or all of the Communication Service categories.

Figure 18. The DTE time history plot shows data usage by Communication Service type based on PNP-

generated trajectories.

During the Task 6 Simulation Assessment we will assess NAS performance using operational scenarios (traffic scenarios) that represent different levels and distributions of future air traffic. This will provide insight into where future data traffic loads and densities may be high depending on the future air traffic as well as the NAS concepts and technologies being represented. In Task 6 we plan to work with NASA and the FAA to leverage prior research in modeling and simulation of the future NAS to define our parameters for the Simulation Assessment.

Final Phase, Impacts, and Future WorkDuring the final phase of this project we will complete

Tasks 3-6. Major plans are as follows:

Task 3: completion of dual-band radio and link layer protocol designs, and laboratory and flight testing. Analysis of flight test results.

Task 4: additional mmWave channel measurements at one or more large airports; completion of mmWave antenna and networking studies.

Task 5: completion of radar measurements, algorithm development for drone detection and classification.

Task 6: development of ATM simulations with new input data and HSCNA capabilities, followed by detailed simulation runs to assess results.

Multiple publications will result from completion of these tasks, in addition to the final project reports. Our plan is to create an online document repository for all our papers and reports. For Task 4 we envision contributions to the 5G Millimeter Wave Channel Model Alliance. We may also organize a future conference special session or workshop to discuss our results and future collaborations. We are also interested in pursuing standardization of some of our techniques.

ConclusionIn this paper we first reviewed the NASA University

Leadership Initiative, which emanated from the Aeronautics Research Mission Directorate’s Strategic Implementation Plan. We described our Hyper-Spectral Communications and Networking for ATM ULI project. This HSCNA project has six major tasks: development of a Concept of Operations for setting context for evaluation of HSCNA impacts; a review of existing/planned CNS communications technologies and identification of improvements; design of multi-band aviation networks for all phases of flight, including new radio testing with Boeing; airport millimeter wave channel modeling and dense network design; design of methods of detection of unauthorized UAS; and evaluation of the HSCNA designs in ATM computer simulations. Tasks 1 and 2 are complete, and our focus for the remainder of the project is on Tasks 3-6. We provided some example results for new multi-band radio designs, millimeter wave wireless channel, antennas, and networking analyses, UAS detection, and the Task 6 ATM performance simulations. A brief discussion of future work was also provided.

AcknowledgmentThe authors thank the NASA ULI program staff for

their support. The authors would also like to thank their post-doctoral research assistants, Ph.D. students, and Master’s students for their contributions. We also thank the FAA for providing 2020 and 2025 future traffic schedules for use in the September 2019 DTE simulation demonstration: Joe Post, James Bonn, Kimberly Noonan, and Sanjiv Shresta.

References

[1] NASA University Leadership Initiative announcement, [online] available https://www.nasa.gov/press-release/university-research-teams-to-study-potential-aeronautical-innovations

[2] ITT Industries, “Technology Assessment for the Future Aeronautical Communications System,” NASA/CR-2005-213587, TR04055, Reston, VA, May 2005.

[3] J. M. Budinger, E. Hall, “Aeronautical mobile airport communications system (AeroMACS),” NASA Technical Report, NASA/TM-2011-217236, October 2011.

[4] H. Jamal, D. W. Matolak, “Performance of L-band Aeronautical Communication System Candidates in the Presence of Multiple DME Interferers,” AIAA/IEEE DASC, Sacramento, CA, 25-29 September 2016.

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2020 Integrated Communications Navigation and Surveillance (ICNS) Conference April 21-23