The management of RF spectrum resources between heterogeneous RF devices has become more challenging with the advent of$5 \mathrm{G}, 6 \mathrm{G}$and the desire to enable more spectrum sharing interactions in different bands. Most of the research on Dynamic Spectrum Access (DSA) algorithms considers non-cooperative scenarios with RF devices using omnidirectional antennas. In this paper, we study the effects of antenna directionality on cooperative DSA. Specifically, we develop a custom simulator for large-scale DSA networks that leverages IEEE 1900.5.2 Spectrum Consumption Models (SCMs) to enable coordination and computation of aggregate interference to deconflict spectrum use in large scale scenarios. SCMs offer a mechanism for RF devices to describe the characteristics of their use of spectrum and their needs in terms of interference protection. We create SCMs for RF systems with directional antennas based on measurements from a directional mmWave antenna and from the operational characteristics defined by the European Telecommunications Standards Institute (ETSI). We leverage these SCMs to perform a comparative analysis of spectrum use efficiency in cooperative DSA networks with up-to 300 links of transmitter-receiver RF devices using omnidirectional antennas vs similar networks using directional antennas with different half-power beam widths. The simulation results show the benefits to spectrum use efficiency that can be achieved with directional antennas and how largescale DSA methods can be studied and designed with the use of SCMs that incorporate detailed characteristics of directional antennas.

Dynamic Spectrum Access (DSA) is a key mechanism for meeting the ever-increasing demand for emerging wireless services. DSA involves managing and assigning available spectrum resources in a way that minimizes interference and allows RF coexistence between heterogeneous devices and systems. Such co-existence mechanisms, if they are to succeed when heterogeneous RF devices managed by different entities need to operate in a given area and frequency band (licensed and/or unlicensed), require a common mechanism for expressing the boundaries of spectrum use of each device so that spectrum use deconfliction methods can be built and verified. Spectrum Consumption Models (SCMs) – defined in the IEEE 1900.5.2 standard – offer a mechanism for RF devices to: (i) declare the characteristics of their intended spectrum use and their interference protection needs; and (ii) determine compatibility (non-interference) with existing devices. In this paper, we propose a novel SCM-based Spectrum Deconfliction (SD) algorithm that dynamically configures RF operational parameters (e.g., center frequency and transmission power) of a target transmitter-receiver pair aiming to minimize interference with existing devices/systems. We also propose sequential and distributed DSA methods that use the SD algorithm for assigning spectrum in large-scale networks. To evaluate the performance of our methods in terms of computation time, spectrum assignment efficiency, and overhead, we use two custom-made simulation platforms. Finally, to experimentally demonstrate the feasibility of our methods, we build a proof-of-concept implementation in the NSF PAWR COSMOS wireless testbed. The results reveal the advantages of using SCMs and their capabilities to conduct spectrum assignments in dynamic and congested communication environments.

Conflicting transactions within blockchain networks not only pose performance challenges but also introduce security vulnerabilities, potentially facilitating malicious attacks. In this paper, we explore the impact of conflicting transactions on blockchain attack vectors. Through modeling and simulation, we delve into the dynamics of four pivotal attacks - block withholding, double spending, balance, and distributed denial of service (DDoS), all orchestrated using conflicting transactions. Our analysis not only focuses on the mechanisms through which these attacks exploit transaction conflicts but also underscores their potential impact on the integrity and reliability of blockchain networks. Additionally, we propose a set of countermeasures for mitigating these attacks. Through implementation and evaluation, we show their effectiveness in lowering attack rates and enhancing overall network performance seamlessly, without introducing additional overhead. Our findings emphasize the critical importance of actively managing conflicting transactions to reinforce blockchain security and performance.

A microwave radiometer (MWR) at NOAA-CESSRST in Manhattan, NYC has experienced interference from nearby sources operating in the 5G FR2 n257 band (26.50--29.50 GHz). In this poster, we produce interference using a mobile 28 GHz IBM Phased Array Antenna Module (PAAM). The mobile PAAM leverages a software-defined radio which offers flexibility in varying center frequency, modulation, gain, bandwidth, time schedule, and more. In this poster, we show preliminary experiments which successfully created controlled interference to a MWR's 28 GHz channel which lead to distortion in some of the MWR final products, such as water vapor profile. We transmitted a 10 MHz bandwidth OFDM signal with varying amplitude, observing the highly sensitive MWR voltage response to fractional dB increments of the transmitter gain. The mobile PAAM is characterized in an anechoic chamber and MWR measurements are taken at various azimuth angles to help estimate the MWR antenna pattern. Future work will develop a Spectrum Consumption Model to help enable coexistence of MWRs and Beyond-5G networks.

Next generation wireless services and applications, including Augmented Reality, Internet-of-Things, and Smart-Cities, will increasingly rely on Dynamic Spectrum Access (DSA) methods that can manage spectrum resources rapidly and efficiently. Advances in regulatory policies, standardization, networking, and wireless technology are enabling DSA methods on a more granular basis in terms of time, frequency, and geographical location which are key for the operation of 5G and beyond-5G networks. In this context, this paper proposes a novel DSA algorithm that leverages IEEE 1900.5.2 Spectrum Consumption Models (SCMs) which offer a mechanism for RF devices to: (i) "announce" or "declare" their intention to use the spectrum and their needs in terms of interference protection; and (ii) determine compatibility (i.e., non-interference) with existing devices. In this paper, we develop an SCM-based DSA algorithm for spectrum deconfliction in large-scale wireless network environments and evaluate this algorithm in terms of computation time, efficiency of spectrum allocation, and number of device reconfigurations due to interference using a custom simulation platform. The results demonstrate the benefits of using SCMs and their capabilities to perform fine grained spectrum assignments in dynamic and dense communication environments.

The number of daily sUAS operations in uncontrolled low altitude airspace is expected to reach into the millions in a few years. Therefore, UAS density prediction has become an emerging and challenging problem. In this paper, a deep learning-based UAS instantaneous density prediction model is presented. The model takes two types of data as input: 1) the historical density generated from the historical data, and 2) the future sUAS mission information. The architecture of our model contains four components: Historical Density Formulation module, UAS Mission Translation module, Mission Feature Extraction module, and Density Map Projection module. The training and testing data are generated by a python based simulator which is inspired by the multi-agent air traffic resource usage simulator (MATRUS) framework. The quality of prediction is measured by the correlation score and the Area Under the Receiver Operating Characteristics (AUROC) between the predicted value and simulated value. The experimental results demonstrate outstanding performance of the deep learning-based UAS density predictor. Compared to the baseline models, for simplified traffic scenario where no-fly zones and safe distance among sUASs are not considered, our model improves the prediction accuracy by up to 15.2% and its correlation score reaches 0.947. In a more realistic scenario, where the no-fly zone avoidance and the safe distance among sUASs are maintained using A* routing algorithm, our model can still achieve 0.822 correlation score. Meanwhile, the AUROC can reach 0.951 for the hot spot prediction.

Small Unmanned Aircraft Systems (sUAS) will be an important component of the smart city and intelligent transportation environments of the near future. The demand for sUAS related applications, such as commercial delivery and land surveying, is expected to grow rapidly in next few years. In general, sUAS traffic scheduling and management functions are needed to coordinate the launching of sUAS from different launch sites and plan their trajectories to avoid conflict while considering several other constraints such as expected arrival time, minimum flight energy, and availability of communication resources. However, as the airbone sUAS density grows in a certain area, it is difficult to foresee the potential airspace and communications resource conflicts and make immediate decisions to avoid them. To address this challenge, we present a temporal and spatial routing algorithm for sUAS trajectory management in a high density urban area. It plans sUAS movements in a spatial and temporal maze with the consideration of obstacles that are either static or dynamic in time. The routing allows the sUAS to avoid static no-fly areas (i.e. static obstacles) or other in-flight sUAS and areas that have congested communication resources (i.e. dynamic obstacles). The algorithm is evaluated using an agent-based simulation platform. The simulation results show that the proposed algorithm outperforms reference route management algorithms in many areas, especially in processing speed and memory efficiency. Detailed comparisons are provided for the sUAS flight time, the overall throughput, the conflict rate and communication resource utilization. The results demonstrate that our proposed algorithm can be used as a solution to improve the efficiency of airspace and communication resource utilization for next generation smart city and smart transportation.

Spectrum consumption models attempt to capture spectral, spatial, and temporal consumption of spectrum of any specific RF transmitter, receiver, system, or collection of systems. The information contained in the models enables better spectrum management practices and allows for the identification of spectrum reuse opportunities. The characteristics and structure of spectrum consumption models (SCMs) are being standardized within the IEEE DySPAN-SC P1900.5.2 group. This paper presents and discusses how SCMs can be used to enable spectrum sharing and new spectrum management interactions. We describe the ongoing efforts to standardize SCMs to ensure that they can: 1) effectively capture the boundaries of spectrum use in all of its dimensions by devices and systems of devices and 2) provide a stable definition on how to arbitrate the compatibility of SCMs. By achieving these objectives, SCMs will enable innovation in spectrum sharing and in the development of dynamic spectrum access systems.

Academic organizations that provide students with the facilities for experimenting and learning basic and advanced concepts in networking rely on computer networking laboratories. These facilities can be implemented in many ways with varying degrees of cost, management complexity and capabilities. The benefits of these facilities can be extended by enabling remote access to them for distance education purposes. Providing remote access capabilities to these facilities with the objective of offering the learning experience of a computer networking laboratory to distance education students of Information and Communication Technology (ICT) programs is a challenging task. This paper provides a description and a comparative analysis of the implementation of three different computer network laboratory setups for which remote access was enabled and discusses lessons learned and good practices for similar setups.