StormWave: An Open-Source Portable SDR Platform for Over-the-Air Resilience Evaluation of Terrestrial and Aerial Communications

This paper presents \emph{StormWave}, an open-source, portable software-defined Radio Frequency (RF) interference generation and monitoring platform designed for realistic field-based evaluation of the resilience of wireless communication systems. StormWave enables seamless composition and runtime switching among a wide range of narrowband and wideband waveforms, while supporting multiple digital modulations, adaptive coding, and multi-radio orchestration with real-time spectrum visualization. We evaluate the effectiveness of StormWave through both outdoor ground and air-to-air (A2A) experiments. Ground experiments demonstrate clear waveform- and modulation-dependent interference effects under realistic propagation conditions, while A2A experiments reveal pronounced distance-dependent constellation distortion and access-symbol degradation under active interference. The StormWave source code will be released to the community, with the expectation that StormWave will be used as a flexible, extensible, and field-ready platform for systematically validating interference resilience of wireless systems under realistic operating conditions.

C-POD: An AWS Cloud Framework for Edge Pod Automation and Remote Wireless Testbed Sharing

This paper presents C-POD, a cloud-native framework that automates the deployment and management of edge pods for seamless remote access and sharing of wireless testbeds. C-POD leverages public cloud resources and edge pods to lower the barrier to over-the-air (OTA) experimentation, enabling researchers to share and access distributed testbeds without extensive local infrastructure. A supporting toolkit has been developed for C-POD to enable flexible and scalable experimental workflows, including containerized edge environments, persistent Secure Shell (SSH) tunnels, and stable graphical interfaces. We prototype and deploy C-POD on the Amazon Web Services (AWS) public cloud to demonstrate its key features, including cloud-assisted edge pod deployment automation, elastic computing resource management, and experiment observability, by integrating two wireless testbeds that focus on RF signal generation and 5G(B) communications, respectively.

On the Effects of Modeling on the Sim-to-Real Transfer Gap in Twinning the POWDER Platform

Digital Twin (DT) technology is expected to play a pivotal role in NextG wireless systems. However, a key challenge remains in the evaluation of data-driven algorithms within DTs, particularly the transfer of learning from simulations to real-world environments. In this work, we investigate the sim-to-real gap in developing a digital twin for the NSF PAWR Platform, POWDER. We first develop a 3D model of the University of Utah campus, incorporating geographical measurements and all rooftop POWDER nodes. We then assess the accuracy of various path loss models used in training modeling and control policies, examining the impact of each model on sim-to-real link performance predictions. Finally, we discuss the lessons learned from model selection and simulation design, offering guidance for the implementation of DT-enabled wireless networks.