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1. Tiny-Twin: A CPU-Native Full-stack Digital Twin for NextG Cellular Networks

   https://doi.org/10.1109/DySPAN69846.2026.11571109  

   Mamaghani, Ali; Ghosh, Ushasi; Shakkottai, Srinivas; Bharadia, Dinesh; Jain, Ish Kumar (May 2026, IEEE)

   Modern wireless applications demand testing environments that capture the full complexity of next-generation (NextG) cellular networks. While digital twins promise realistic emulation, existing solutions often compromise on physical-layer fidelity and scalability or depend on specialized hardware. We present Tiny-Twin, a CPU-Native, full-stack digital twin framework that enables realistic, repeatable 5G experimentation on commodity CPUs. Tiny-Twin integrates time-varying multi-tap convolution with a complete 5G protocol stack, supporting plug-and-play replay of diverse channel traces. Through a redesigned software architecture and system-level optimizations Tiny-Twin supports fine-grained convolution entirely in software. With built-in real-time RIC integration and per User Equipment (UE) channel isolation, it facilitates rigorous testing of network algorithms and protocol designs. Our evaluation shows that Tiny-Twin scales to multiple concurrent UEs while preserving protocol timing and end-to-end behavior, delivering a practical middle ground between low-fidelity simulators and high-cost hardware emulators. We release Tiny-Twin as an open-source platform to enable accessible, high-fidelity experimentation for NextG cellular research. 

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2. DT-CoVeSS: Advancing NextG C-V2X Security Evaluation through High-Fidelity Digital Twinning

   https://doi.org/10.1109/INFOCOMWKSHPS65812.2025.11152971  

   Twardokus, Geoff; Rahbari, Hanif (May 2025, IEEE)

   In NextG smart transportation, connected vehicles (CVs) promise to enhance roadway safety and efficiency, but only if the underlying Cellular Vehicle-to-Everything (C-V2X) communications are secure and reliable. However, realistic evaluation of C-V2X security is extremely challenging, as simulators cannot fully capture the nuances of CV environments, while field tests are risky, expensive, and legally restricted. As a bridge between these restrictive options, in this paper we present a zero-risk, low-cost, and highly realistic Digital Twin for Connected Vehicle Safety and Security (DT-CoVeSS), featuring multiple advancements that bring the benefits of digital twins to sidelink C-V2X for the first time. These include (1) automated digital twin construction based on open-source geospatial data; (2) high-fidelity emulation of multipath signal propagation for these rich scattering environments, considering the specific waveform properties of C-V2X; (3) support for commercial C-V2X hardware "in the loop"' to study actual device behavior under various attacks without risking human safety; and (4) an open-source implementation of multi-layer C-V2X services, compliant with current standards like IEEE 1609.2 for security, to enable scalable, repeatable, and comprehensive testing. DT-CoVeSS facilitates realistic exploration of threats in the CV ecosystem; for example, we demonstrate its use to gain deeper insights into jamming efficacy against C-V2X. Moreover, we show our channel twinning even offers a 73% runtime advantage over alternative approaches while capturing factors like wireless scattering that alternatives do not consider, moving closer to real-time evaluation of real-world CV security. 

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3. POST: Programmable Open Spectrum Twin for Enhanced Spectrum Sharing in 6G Bands

   https://doi.org/10.1109/MCOMSTD.2025.3645000  

   Seth, Sayanta; Niloy, Ta-Seen Reaz; Hassan, Md Zoheb; Shah, Vijay K (January 2025, IEEE Communications Standards Magazine)

   This paper puts forward an advanced dynamic spectrum sharing framework, POST (Programmable Open Spectrum Twin) for 6G bands. By combining O-RAN (Open Radio Access Network) and DT (Digital Twin) technologies, POST addresses the limitations of legacy spectrum-sharing systems like TV White Space (TVWS) and Spectrum Access System (SAS) and even recent developments like Proactive Spectrum Adaptation Scheme (ProSAS). Key features of POST include real-time monitoring and prediction of spectrum-sharing scenarios, creation of new spectrum opportunities for 6G cellular infrastructure and the future provision of protection of incumbent users in the mid bands. POST integrates AI/ML for intelligent decision-making, enabling dynamic resource optimization and proactive interference management. Unlike the state-of-the-art, POST offers enhanced real-time adaptability, improved interference mitigation, scalability in complex environments, and “what-if” simulation capabilities for policy testing. These innovations position POST as a crucial technology for fostering sustainable spectrum utilization and establishing a robust foundation for 6G and beyond networks. 

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4. A Co-Operative Perception System for Collision Avoidance Using C-V2X and Client–Server-Based Object Detection

   https://doi.org/10.3390/s25175544  

   Park, Jungme; Kavathekar, Vaibhavi; Bhuduri, Shubhang; Amin, Mohammad Hasan; Devaraj, Sriram Sanjeev (September 2025, Sensors)

   Martín-Sacristán, David; Garcia-Roger, David (Ed.)

   With the recent 5G communication technology deployment, Cellular Vehicle-to-Everything (C-V2X) significantly enhances road safety by enabling real-time exchange of critical traffic information among vehicles, pedestrians, infrastructure, and networks. However, further research is required to address real-time application latency and communication reliability challenges. This paper explores integrating cutting-edge C-V2X technology with environmental perception systems to enhance safety at intersections and crosswalks. We propose a multi-module architecture combining C-V2X with state-of-the-art perception technologies, GPS mapping methods, and the client–server module to develop a co-operative perception system for collision avoidance. The proposed system includes the following: (1) a hardware setup for C-V2X communication; (2) an advanced object detection module leveraging Deep Neural Networks (DNNs); (3) a client–server-based co-operative object detection framework to overcome computational limitations of edge computing devices; and (4) a module for mapping GPS coordinates of detected objects, enabling accurate and actionable GPS data for collision avoidance—even for detected objects not equipped with C-V2X devices. The proposed system was evaluated through real-time experiments at the GMMRC testing track at Kettering University. Results demonstrate that the proposed system enhances safety by broadcasting critical obstacle information with an average latency of 9.24 milliseconds, allowing for rapid situational awareness. Furthermore, the proposed system accurately provides GPS coordinates for detected obstacles, which is essential for effective collision avoidance. The technology integration in the proposed system offers high data rates, low latency, and reliable communication, which are key features that make it highly suitable for C-V2X-based applications. 

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5. Look Once, Beam Twice: Camera-Primed Real-Time Double-Directional mmWave Beam Management for Vehicular Connectivity

   Biswas, A; Pramanik, A; Ekici, E; Vuran, M C (June 2026, IEEE SECON 2026)

   Millimeter-wave (mmWave) frequencies promise multi-gigabit connectivity for vehicle-to-everything (V2X) networks, but face challenges in terms of severe path loss and mobility-related beam misalignment. Reliable V2X connectivity requires fast, double-directional beam alignment. However, existing methods suffer from high training overhead and limited generalization to unseen scenarios. This paper presents VIsion-based BEamforming (VIBE), a hybrid model-based, closed-loop, learning architecture for real-time double-directional mmWave beam management primed by camera sensing. VIBE fuses machine learning, model-based reasoning, and closed-loop RF feedback to -24dBm-98dBm balance beam-pair establishment latency with link quality. VIBE bypasses exhaustive training overhead and accelerates link establishment by leveraging camera observations to reduce the beam search space. Lightweight beam refinement and offset tracking mechanisms adaptively refine beams in response to dynamic application requirements. VIBE is implemented and evaluated across online indoor/outdoor testbeds, public datasets, and real-time vehicular experiments, demonstrating strong generalization capabilities, making it suitable for real-time V2X communication. Comparisons with 5G NR hierarchical beamforming show that VIBE consistently maintains lower outage rates. Furthermore, VIBE outperforms state-of-the-art end-to-end ML models for beam selection when evaluated on public datasets and achieves outage rates as low as 1.1–1.4%. The results show that a hybrid model-based, closed-loop learning architecture is better suited for real-world mmWave vehicular connectivity than end-to-end trained ML models. For reproducibility, we publish our code to https://github.com/UNL-CPN-Lab/Look-Once-Beam-Twice. 

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