This demo introduces WaveBox, a dynamic, software-defined waveform generation system developed to assess the resilience of communication networks against many types of interference scenarios. WaveBox features seamless waveform switching, allowing users to efficiently adjust interference patterns to adapt to diverse operational scenarios. We will showcase the system's effectiveness and versatility, highlighting its ability to adapt to evolving mission requirements. Additionally, the system's intuitive graphical user interface (GUI) supports rapid waveform adjustments, enhancing its responsiveness in dynamic environments. WaveBox can provide a flexible software-defined tool for evaluating the robustness of wireless systems.

Ensuring the safety and reliability of emerging Advanced Aerial Mobility (AAM) systems requires wireless communication to provide real-time monitoring and control information to ground stations. This greatly depends on the quality of the wireless links during aerial transit. In this demonstration, we present AirTwinX, designed to emulate flight control of flying vehicles while generating high-fidelity, context-aware models for air-to-air (AA) and air-to-ground (AG) communication links. Using GPU-accelerated ray tracing, AirTwinX can predict the quality of wireless links with near real-time updates based on the environmental geometry observed during flight. This data is then used to guide autonomous control decision-making. Additionally, the vehicle control toolchain employed in this work is based on software-in-the-loop (SITL) emulation of a commercial flight controller, enabling seamless translation of control policies from simulation to real-world hardware.

In highly dynamic and contested RF environments, communication systems must swiftly adapt to fluctuating spectral conditions while ensuring network quality of service (QoS). Maintaining link synchronization and spectral efficiency during waveform adaptation is particularly challenging due to the high mobility and autonomy of devices, coupled with the possibility of operating in GPS-denied environments. In this demo, we introduce a scalable, high-speed FPGA-based parallel decoding algorithm that leverages the HORNets signature adaptation protocol to address these challenges. Our solution preserves link synchronization within a multi-node network and enables efficient, lightweight waveform adaptation without reliance on GPS. The algorithm's resilience and effectiveness are demonstrated using a three-node cluster configuration, all subjected to non-colored or colored intentional interference.

In this work, we present a new federation framework for Union-Labs, an innovative cloud-based resource-sharing infrastructure designed for next-generation (NextG) and Internet of Things (IoT) over-the-air (OTA) experiments. The framework aims to reduce the federation complexity for testbeds developers by automating tedious backend operations, thereby providing scalable federation and remote access to various wireless testbeds. We first describe the key components of the new federation framework, including the Systems Manager Integration Engine (SMIE), the Automated Script Generator (ASG), and the Database Context Manager (DCM). We then prototype and deploy the new Federation Plane on the Amazon Web Services (AWS) public cloud, demonstrating its effectiveness by federating two wireless testbeds: i) UB NeXT, a 5G-and-beyond (5G+) testbed at the University at Buffalo, and ii) UT IoT, an IoT testbed at the University of Utah1.

In this article we present a new RFID antenna design based on conductive thermal transfer printing. We first describe the key antenna design parameters, including antenna shape, dimensions, and number of lobes, among others. The desirable range of these parameters have been determined empirically through an extensive experimentation campaign for impedance matching. Then, we print the antenna using conductive Aluminum ribbon on PET coated paper. We evaluate the performance of the new antenna by comparing it with 13 existing benchmark antennas using a commercial RFID reader at 915 MHz. It is found that the new antenna design can achieve better range-angle trade-off than the benchmarks.

Data-driven control based on AI/ML techniques has a great potential to enable zero-touch automated modeling, optimization and control of complex wireless systems. However, it is challenging to collect network traces in the real world because of high time and labor cost, weather limitations as well as safety concerns. In this work we attempt to tackle this challenge by designing a multi-fidelity simulator taking wireless Unmanned Aerial Vehicle (UAV) networks into consideration. We design the simulator by interfacing two Unmanned Aerial System (UAS) simulators we have developed in prior years: UBSim and UB-ANC. The former focuses on UAV network optimization and policy training by considering explicitly the network environments such as blockage dynamics, while the latter focuses more on high-fidelity UAV flight control. We first develop a coordination interface referred to as SimSocket for signaling exchanges between UBSim and UB-ANC in simulations, and then showcase coordinated simulations based on UBSim and UB-ANC. The new research that can be enabled by the integrated simulator is also discussed for digital twin-based UAS systems.

Terahertz (THz)-band communications have been envisioned as a key technology to support ultra-high-data-rate applications in 5G-beyond (or 6G) wireless networks. Compared to the microwave and mmWave bands, the main challenges with the THz band are in its i) large path loss hence limited network coverage and ii) visible-light-like propagation characteristics hence poor support of mobility in blockage-rich environments. This paper studies quantitatively the applicability of THz-band communications in blockage-rich mobile environments, focusing on a new network scenario calledFlyTera. InFlyTera, a set of hotspots mounted on flying drones collaboratively provide data streaming services to ground users, in the microwave, mmWave and THz bands. We first provide a mathematical formulation of theFlyTeracontrol problem, where the objective is to maximize the network spectral efficiency by jointly controlling the flight of the drone hotspots, their association to the ground users, and the spectrum bands used by the users. To solve the resulting problem, which is shown to be a mixed integer nonlinear nonconvex programming (MINLP) problem, we design distributed solution algorithms based on a combination of echo state learning and reinforcement learning. An extensive simulation campaign is then conducted with SimBAG, a newly developedSimulator ofBroadbandAerial-Ground wireless networks. It is shown that no single spectrum band can meet the requirements of high data rate and wide coverage simultaneously. Moreover, from the network-level point of view, THz-band communications can significantly benefit from the mobility of the flying drones, and on average$4 - 6$timeshigher (rather than lower)throughput can be achieved in mobile than in static environments.