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How Can UAVs Balance Sensing and Communication in Future Vehicular Networks?​

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Future vehicular networks will need to do more than exchange data between vehicles, infrastructure, and users. They will also need to sense the surrounding environment in real time, supporting applications such as traffic monitoring, vehicle detection, and intelligent mobility management. This creates an important challenge for next-generation wireless systems: how can one platform provide both reliable communication and accurate sensing without wasting limited wireless resources?

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New accepted paper in IEEE Internet of Things Magazine

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We are pleased to announce that a new paper from LINCS Lab has been accepted for publication in IEEE Internet of Things Magazine.
The paper, titled “Securing the Future of IoMT in the Post-Quantum Era: An Edge-Native Federated Learning Approach,” is authored by Taym Alshoghri, Deemah H. Tashman, Mohammad Reza Gerami, and Prof. Soumaya Cherkaoui.

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LINCS Lab at ICC 2026 in Glasgow with Six Research Papers​!

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LINCS Lab is proud to announce its strong presence at the IEEE International Conference on Communications (ICC) 2026, held in Glasgow, Scotland, with six research papers accepted at this major international conference in communications and networking.
IEEE ICC is one of the flagship international conferences of the IEEE Communications Society, bringing together researchers, academics, and industry professionals from around the world to exchange the latest advances in communications technologies.

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LINCS Lab to Present Two Papers at IEEE IWCMC 2026

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LINCS Lab is pleased to announce that two papers from the lab have been accepted at the 22nd International Wireless Communications & Mobile Computing Conference (IWCMC 2026). The papers address key challenges in next-generation wireless systems, including RIS-assisted 6G IoT resource allocation in the FR3 spectrum and adversarial robustness in AI-driven RAN slicing.

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New Paper Published in International Conference on Quantum Communications, Networking, and Computing (QCNC 2026)​

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We are pleased to announce that our paper, “Learning Gaussian Processes with Randomized Quantum Local Kernels,” has been published in International Conference on Quantum Communications, Networking, and Computing (QCNC 2026).

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Making Open RAN Intelligent: The Role of Machine Learning​

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O-RAN is fundamentally data-driven. Its intelligent control loops—enabled by its Near-Real-Time and Non-Real-Time RAN Intelligent Controllers (RICs)— support continuous monitoring, closed-loop control, and policy-driven optimization. These capabilities create a natural foundation for machine learning, allowing networks to learn from data, adapt to dynamic conditions, and make informed decisions in real time.

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