Evaluating cellular systems, from 5th generation (5G) New Radio (NR) and 5G-Advanced to 6th generation (6G), is challenging because the performance emerges from the tight coupling of propagation, beam management, scheduling, and higher-layer interactions. System-level simulation is therefore indispensable, yet the vast majority of studies rely on the statistical 3rd Generation Partnership Project (3GPP) channel models. These are well suited to capture average behavior across many statistical realizations, but cannot reproduce site-specific phenomena such as comer diffraction, street-canyon blockage, or deterministic line-of-sight conditions and angle-of- departure/arrival relationships that drive directional links.This paper extends 5G-LENA, an NR module for the system-level Network Simulator 3 (ns-3), with a trace-based channel model that processes the Multipath Components (MPCs) obtained from external ray-tracers (e.g., Sionna Ray Tracer (RT)) or measurement campaigns. Our module constructs frequency-domain channel matrices, and feeds them to the existing Physical (PHY)/Medium Access Control (MAC) stack without any further modifications. The result is a geometry-based channel model that remains fully compatible with the standard 3GPP implementation in 5G-LENA, while delivering site-specific geometric fidelity. This new module provides a key building block toward Digital Twin (DT) capabilities by offering realistic site-specific channel modeling, unlocking studies that require site awareness, including beam management, blockage mitigation, and environment-aware sensing. We demonstrate its capabilities for precise beam-steering validation and end-to-end metric analysis. In both cases, the trace-driven engine exposes performance inflections that the statistical model does not exhibit, confirming its value for high- fidelity system-level cellular networks research and as a step toward DT applications.

Accurate, low-latency channel modeling is essential for real-time wireless network simulation and digital-twin applications. Traditional modeling methods like ray tracing are however computationally demanding and unsuited to model dynamic conditions. In this paper, we propose AIRMap, a deep-learning framework for ultra-fast radio-map estimation, along with an automated pipeline for creating the largest radio-map dataset to date. AIRMap uses a single-input U-Net autoencoder that processes only a 2D elevation map of terrain and building heights. Trained on 1.2M Boston-area samples and validated across four distinct urban and rural environments with varying terrain and building density, AIRMap predicts path gain with under 4 dB RMSE in 4 ms per inference on an NVIDIA L40S-over 100x faster than GPU-accelerated ray tracing based radio maps. A lightweight calibration using just 20% of field measurements reduces the median error to approximately 5%, significantly outperforming traditional simulators, which exceed 50% error. Integration into the Colosseum emulator and the Sionna SYS platform demonstrate near-zero error in spectral efficiency and block-error rate compared to measurement-based channels. These findings validate AIRMap's potential for scalable, accurate, and real-time radio map estimation in wireless digital twins.

Open Radio Access Networks (RANs) leverage disaggregated and programmable RAN functions and open interfaces to enable closed-loop, data-driven radio resource management. This is performed through custom intelligent applications on the RAN Intelligent Controllers (RICs), optimizing RAN policy scheduling, network slicing, user session management, and medium access control, among others. In this context, we have proposed dApps as a key extension of the O-RAN architecture into the real-time and user-plane domains. Deployed directly on RAN nodes, dApps access data otherwise unavailable to RICs due to privacy or timing constraints, enabling the execution of control actions within shorter time intervals. In this paper, we propose for the first time a reference architecture for dApps, defining their life cycle from deployment by the Service Management and Orchestration (SMO) to real-time control loop interactions with the RAN nodes where they are hosted. We introduce a new dApp interface, E3, along with an Application Protocol (AP) that supports structured message exchanges and extensible communication for various service models. By bridging E3 with the existing O-RAN E2 interface, we enable dApps, xApps, and rApps to coexist and coordinate. These applications can then collaborate on complex use cases and employ hierarchical control to resolve shared resource conflicts. Finally, we present and open-source a dApp framework based on OpenAirInterface (OAI). We benchmark its performance in two real-time control use cases, i.e., spectrum sharing and positioning in a 5th generation (5G) Next Generation Node Base (gNB) scenario. Our experimental results show that standardized real-time control loops via dApps are feasible, achieving average control latency below 450 microseconds and allowing optimal use of shared spectral resources.

Radio Frequency Fingerprinting through Deep Learning (RFFDL) is a data-driven IoT authentication technique that leverages the unique hardware-level manufacturing imperfections associated with a particular device to recognize (“fingerprint”) the device itself based on variations introduced in the transmitted waveform. Key impediments in developing robust and scalable RFFDL techniques that are practical in dynamic and mobile environments are the non-stationary behavior of the wireless channel and other impairments introduced by the propagation conditions. To date, the existing RFFDL-based techniques have only been able to demonstrate a desirable performance when the training and testing environment remains the same, which makes the solutions impractical.SignCRFbrings to the RFFDL landscape what it has been missing so far: a scalable, channel-agnostic data-driven radio authentication platform with unmatched precision in fingerprinting wireless devices based on their unique manufacturing impairments that isindependent of the dynamic nature of the environment or channel irregularities caused by mobility.SignCRFconsists of: (i) a classifier developed in a base-environment with minimum channel dynamics, and finely trained to authenticate devices with high accuracy and at scale; (ii) an environment translator that is carefully designed and trained to remove the dynamic channel impact from RF signals while maintaining the radio's specific “signature”; and (iii) a Max Rule module that selects the highest precision authentication technique between the baseline classifier and the environment translator per radio. We design, train, and validate the performance ofSignCRFfor multiple technologies in dynamic environments and at scale (100 LoRa and 20 WiFi devices, the largest datasets available in the literature). We assess the scalability ofSignCRFacross various testbed scales by validating our system using small, medium, and large-scale testbeds, with sizes of 5, 20, and 100 devices, respectively. We demonstrate thatSignCRFcan significantly improve the RFFDL performance by achieving as high as 100% correct authentication for WiFi devices and 80% correctly authenticated LoRa devices, a 5x and 8x improvement when compared to the state-of-the-art respectively. Furthermore, we show thatSignCRFis resilient to adversarial actions by reducing the device recognition accuracy from 73% to 6%, which translates into zero mis-authentication of adversary radios that try to impersonate legitimate devices, which has not been achieved by any prior RFFDL techniques.

This paper introduces an innovative framework designed for progressive (granular in time to onset) prediction of seizures through the utilization of a Deep Learning (DL) methodology based on non-invasive multimodal sensor networks. Epilepsy, a debilitating neurological condition, affects an estimated 65 million individuals globally, with a substantial proportion facing drug-resistant epilepsy despite pharmacolog-ical interventions. To address this challenge, we advocate for predictive systems that provide timely alerts to individuals at risk, enabling them to take precautionary actions. Our framework employs advanced DL techniques and uses personalized data from a network of non-invasive electroencephalogram (EEG) and electrocardiogram (ECG) sensors, thereby enhancing prediction accuracy. The algorithms are optimized for real-time processing on edge devices, mitigating privacy concerns and minimizing data transmission overhead inherent in cloud-based solutions, ultimately preserving battery energy. Additionally, our system predicts the countdown time to seizures (with 15-minute intervals up to an hour prior to the onset), offering critical lead time for preventive actions. Our multimodal model achieves 95% sensitivity, 98% specificity, and 97% accuracy, averaged among 29 patients.

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.

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.

Digital twins are now a staple of wireless networks design and evolution. Creating an accurate digital copy of a real system offers numerous opportunities to study and analyze its performance and issues. It also allows designing and testing new solutions in a risk-free environment, and applying them back to the real system after validation. A candidate technology that will heavily rely on digital twins for design and deployment is 6G, which promises robust and ubiquitous networks for eXtended Reality (XR) and immersive communications solutions. In this paper, we present BostonTwin, a dataset that merges a high-fidelity 3D model of the city of Boston, MA, with the existing geospatial data on cellular base stations deployments, in a ray-tracing-ready format. Thus, BostonTwin enables not only the instantaneous rendering and programmatic access to the building models, but it also allows for an accurate representation of the electromagnetic propagation environment in the real-world city of Boston. The level of detail and accuracy of this characterization is crucial to designing 6G networks that can support the strict requirements of sensitive and high-bandwidth applications, such as XR and immersive communication.

Softwarized and programmable Radio Access Networks (RANs) come with virtualized and disaggregated components, increasing the supply chain robustness and the flexibility and dynamism of the network deployments. This is a key tenet of Open RAN, with open interfaces across disaggregated components specified by the O-RAN ALLIANCE. It is mandatory, however, to validate that all components are compliant with the specifications and can successfully interoperate, without performance gaps with traditional, monolithic appliances. Open Testing & Integration Centers (OTICs) are entities that can verify such interoperability and adherence to the standard through rigorous testing. However, how to design, instrument, and deploy an OTIC which can offer testing for multiple tenants, heterogeneous devices, and is ready to support automated testing is still an open challenge. In this paper, we introduce a blueprint for a programmable OTIC testing infrastructure, based on the design and deployment of the Open6G OTIC at Northeastern University, Boston, and provide insights on technical challenges and solutions for O-RAN testing at scale.

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).

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.

With more than 65M people affected by epilepsy worldwide, early prediction and response to seizure onsets have become more important than ever. Cutting-edge research in implantable medical devices (IMDs) has shown that deep neural networks (DNNs) applied to intracranial electroencephalogram (iEEG) data can predict seizures up to an hour before onset. However, offloading of iEEG data to the edge/cloud is highly prohibitive, due to the sheer size of the generated data. Existing work either focuses on the DNN training phase only, or does not consider the severe energy/space limitations of IMDs. Moreover, the technical aspects of patient personalization, which allows for patient-specific hyper-parameter tuning, still remain unaddressed. In this paper, we propose a platform called AiEEG for in vivo early seizure prediction, whose DNN hardware circuitry can be reconfigured remotely without surgery. We prototype AiEEG on a system on chip (SoC) platform and demonstrate its end-to-end capabilities in seizure prediction with a population of 30 epileptic patients, with iEEG signals coming from a real-world dataset. Extensive experimental results shows that (i) our embedded and personalized DNN has an area under the curve (AUC) averaging at 0.97 and as low as zero false positives per hour (FPH) for over half the patients, an improvement of about 3.5x with respect to a non-personalized prediction method and the best for a dataset of this size when compared to the state-of-the-art; (ii) our AiEEG platform consumes 4.2x less energy than a cloud-based approach, leading to a 4x battery lifetime improvement; (iii) we are able to remotely fine-tune the DNN through partial reconfiguration as needed in about 10s.

Practical experimentation and prototyping are core steps in the development of any wireless technology. Often times, however, this crucial step is confined to small laboratory setups that do not capture the scale of commercial deployments and do not ensure result reproducibility and replicability, or it is skipped altogether for lack of suitable hardware and testing facilities. Recent years have seen the development of publicly-available testing platforms for wireless experimentation at scale. Examples include the testbeds of the PAWR program and Colosseum, the world's largest wireless network emulator. With its 256 software-defined radios, 24 racks of powerful compute servers and first-of-its-kind channel emulator, Colosseum allows users to prototype wireless solutions at scale, and guarantees reproducibility and replicability of results. This tutorial provides an overview of the Colosseum platform. We describe the architecture and components of the testbed as a whole, and we then showcase how to run practical experiments in diverse scenarios with heterogeneous wireless technologies (e.g., Wi-Fi and cellular). We also emphasize how Colosseum experiments can be ported to different testing platforms, facilitating full-cycle experimental wireless research: design, experiments and tests at scale in a fully controlled and observable environment and testing in the field. The tutorial concludes with considerations on the flexible future of Colosseum, focusing on its planned extension to emulate larger scenarios and channels at higher frequency bands (mmWave).

In vivo wireless nanosensor networks (iWNSNs) consist of communicating miniature devices with unprecedented sensing and actuation capabilities, which are able to operate inside the human body. iWNSNs are the basis of emerging healthcare applications, such as intrabody health-monitoring and control of biological processes at subcellular level. Major progress in the field of nanoelectronics, nanophotonics, and wireless communication is enabling the interconnection of the nanodevices in iWNSNs. In this paper, the effect of single biological cells and cell assemblies on the propagation of optical wave for intrabody communications of nanosensors is analytically investigated in three distinct ways, namely, geometrical, time-domain, and frequency-domain analyses. The analytical channel model is validated by means of full wave electromagnetic simulations through a case study for red blood cells (RBCs) inside the blood plasma. The results show that RBCs perform as optical microlenses that confine the radiated light on a focal area, which agrees with recent experimental achievements. It is also shown that changes in shape and size of the cells slightly alter the channel impulse response. This study motivates the development of new communication solutions for intrabody nanoscale optical communication networks and new nanobiosensing strategies able to identify diseases which cause cell shape alterations.

In vivo Wireless Nanosensor Networks (iWNSNs) consist of nano-sized communicating devices with unprecedented sensing and actuation capabilities, which are able to operate inside the human body. Major progress in the field of nanoelectronics, nanophotonics and wireless communication is enabling the communication among nanosensors. Among others, plasmonic nanolasers with sub-micrometric footprint, plasmonic nano-antennas able to confine light in nanometric structures, and single-photon detectors with unrivaled sensitivity, enable the communication among implanted nanosensors in the near infrared and optical transmission window. In this paper, a channel model for in vivo optical communication in iWNSNs is developed. By following a geometrical approach to trace and aggregate the path loss and time delay of each of the rays that encounter a biological cell, a closed form channel impulse response is derived. The analytical channel model is validated by means of electromagnetic simulations for a Red Blood Cell (RBC) inside the blood plasma. The results show that RBCs perform as optical micro-lenses in terms of confining the light that is being radiated to them on a focal line right after the cell. This results are in strong agreement with the recent experimental achievements on interactions of light and RBCs.

Wireless Nanosensor Networks (WNSNs), i.e., networks of miniaturized devices with unprecedented sensing capabilities, are at the basis of transformative applications in the biomedical, environmental and industrial fields. Recent developments in plasmonic nano-antennas point to the Terahertz (THz) band (0.1-10 THz) as the frequency range of communication among nanosensors. While this potentially enables extremely high data rates in WNSNs, the very high path-loss at such frequencies and the limited power of energy-harvesting nano-devices limit the achievable throughput. In this paper, the link throughput maximization problem in WNSNs is addressed by taking into account the device and communication interdependencies in WNSNs. The optimal data packet size which maximizes the link efficiency is derived by capturing the device, channel, physical and link layer peculiarities of WNSNs. The energy harvesting limits and the successful packet transmission time are defined as the optimization problem constraints, and the optimal solution is derived by using a bisection method. Numerical results are provided to analyze the impact of the packet size for different error control strategies. The results show that the optimal packet size quickly decreases with the transmission distance, approaching several hundreds bits for distances beyond a few millimeters.