The wireless spectrum’s increasing complexity poses challenges and opportunities, highlighting the necessity for real-time solutions and robust data processing capabilities. Digital Twin (DT), virtual replicas of physical systems, integrate real-time data to mirror their real-world counterparts, enabling precise monitoring and optimization. Incorporating DTs into wireless communication enhances predictive maintenance, resource allocation, and troubleshooting, thus bolstering network reliability. Our paper introduces TwiNet, enabling bidirectional, near-real-time links between real-world wireless spectrum scenarios and DT replicas. Utilizing the protocol, MQTT, we can achieve data transfer times with an average latency of 14 ms, suitable for real-time communication. This is confirmed by monitoring real-world traffic and mirroring it in real-time within the DT’s wireless environment. We evaluate TwiNet’s performance in two distinct use cases: (i) enhancing Safe Adaptive Data Rate (SADR) systems by assessing risky traffic configurations of UEs, resulting in approximately 15% improved network performance compared to original network selections; and (ii) deploying new CNNs in response to jammed pilots, where the DL pipeline achieves up to 97% accuracy by training on artificial data and deploying a new model in as low as 2 minutes to counter persistent adversaries. TwiNet enables swift deployment and adaptation of DTs, addressing crucial challenges in modern wireless communication systems.

Spectrum has become an extremely scarce and congested resource. As a consequence, spectrum sensing enables the coexistence of different wireless technologies in shared spectrum bands. Most existing work requires spectrograms to classify signals. Ultimately, this implies that images need to be continuously created from I/Q samples, thus creating unacceptable latency for real-time operations. In addition, spectrogram-based approaches do not achieve sufficient granularity level as they are based on object detection performed on pixels and are based on rectangular bounding boxes. For this reason, we propose a completely novel approach based on semantic spectrum segmentation, where multiple signals are simultaneously classified and localized in both time and frequency at the I/Q level. Conversely from the state-of-the-art computer vision algorithm, we add non-local blocks to combine the spatial features of signals, and thus achieve better performance. In addition, we propose a novel data generation approach where a limited set of easy-to-collect real-world wireless signals are "stitched together" to generate large-scale, wideband, and diverse datasets. Experimental results obtained on multiple testbeds (including the Arena testbed) using multiple antennas, multiple sampling frequencies, and multiple radios over the course of 3 days show that our approach classifies and localizes signals with a mean intersection over union (IOU) of 96.70% across 5 wireless protocols while performing in real-time with a latency of 2.6 ms. Moreover, we demonstrate that our approach based on non-local blocks achieves 7% more accuracy when segmenting the most challenging signals with respect to the state-of-the-art U-Net algorithm. We will release our 17 GB dataset and code.

Digital Twins (DTs) have become predominant for wireless spectrum emulation thanks to their realistic virtual mod-e~ing to test and optimize wireless network performance, enabling efficient spectrum management. In this work, we propose and demonstrate a bidirectional, near-real-time communication link between real-world wireless spectrum scenarios and DT replicas by utilizing the MQTT protocol. Results show that our link can achieve data transfer times with an average latency of 14 ms, which is suitable for real-time communication, allowing for our DT to be within the required latency threshold for applications such as voice communication, video streaming, and Internet of Thin2:8 (loT) control.

Wireless network emulators are being increasingly used for developing and evaluating new solutions for Next Generation (NextG) wireless networks. However, the reliability of the solutions tested on emulation platforms heavily depends on the precision of the emulation process, model design, and parameter settings. To address, obviate, or minimize the impact of errors of emulation models, in this work, we apply the concept of Digital Twin (DT) to large-scale wireless systems. Specifically, we demonstrate the use of Colosseum, the world?s largest wireless network emulator with hardware-in-the-loop, as a DT for NextG experimental wireless research at scale. As proof of concept, we leverage the Channel emulation scenario generator and Sounder Toolchain (CaST) to create the DT of a publicly available over-the-air indoor testbed for sub-6 GHz research, namely, Arena. Then, we validate the Colosseum DT through experimental campaigns on emulated wireless environments, including scenarios concerning cellular networks and jamming of Wi-Fi nodes, on both the real and digital systems. Our experiments show that the DT is able to provide a faithful representation of the real-world setup, obtaining an average similarity of up to 0.987 in throughput and 0.982 in Signal to Interference plus Noise Ratio (SINR).

Due to the increased usage of spectrum caused by the exponential growth of wireless devices, detecting and avoiding interference has become an increasingly relevant problem to ensure uninterrupted wireless communications. In this paper, we focus our interest on detecting narrowband interference caused by signals that, despite occupying a small portion of the spectrum only, can cause significant harm to wireless systems. For example, in the case of interference with pilots and other signals that are used to equalize the effect of the channel or attain synchronization. Due to the small sizes of these signals, detection can be difficult due to their low energy footprint, while greatly impacting (or denying completely in some cases) network communications. We present a novel narrowband interference detection solution that utilizes convolutional neural networks (CNNs) to detect and locate these signals with high accuracy. To demonstrate the effectiveness of our solution, we have built a prototype that has been tested and validated on a real-world over-the-air large-scale wireless testbed. Our experimental results show that our solution is capable of detecting narrowband jamming attacks with an accuracy of up to 99%. Moreover, it is also able to detect multiple attacks affecting several frequencies at the same time even in the case of previously unseen attack patterns. Not only can our solution achieve a detection accuracy between 92% and 99%, but it does so by only adding an inference latency of 0.093ms.

Jamming attacks have plagued wireless communication systems and will continue to do so going forward with technological advances. These attacks fall under the category of Electronic Warfare (EW), a continuously growing area in both attack and defense of the electromagnetic spectrum, with one subcategory being electronic attacks (EA). Jamming attacks fall under this specific subcategory of EW as they comprise adversarial signals that attempt to disrupt, deny, degrade, destroy, or deceive legitimate signals in the electromagnetic spectrum. While jamming is not going away, recent research advances have started to get the upper hand against these attacks by leveraging new methods and techniques, such as machine learning. However, testing such jamming solutions on a wide and realistic scale is a daunting task due to strict regulations on spectrum emissions. In this paper, we introduce eSWORD (emulation (of) Signal Warfare On Radio-frequency Devices), the first large-scale framework that allows users to safely conduct real-time and controlled jamming experiments with hardware-in-the-loop. This is done by integrating METEOR, an electronic warfare (EW) threat-emulating software developed by the MITRE Corporation, into the Colosseum wireless network emulator that enables large-scale experiments with up to 49 software-defined radio nodes. We compare the performance of eSWORD with that of real-world jamming systems by using an over-the-air wireless testbed (considering safe measures when conducting experiments). Our experimental results demonstrate that eSWORD achieves up to 98% accuracy in following throughput, signal-to-interference-plus-noise ratio, and link status patterns when compared to real-world jamming experiments, testifying to the high accuracy of the emulated eSWORD setup.