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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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Quantum-Aided Active User Detection for Energy-Efficient CD-NOMA in Cognitive Radio Networks

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As wireless networks evolve toward the 6G era, they face growing challenges in managing the immense number of connected devices while maintaining energy efficiency and spectrum utilization. In a recent IEEE IWCMC 2025 publication,  “Quantum-Aided Active User Detection for Energy-Efficient CD-NOMA in Cognitive Radio Networks,” Deemah H. Tashman and Prof. Soumaya Cherkaoui propose a quantum-enhanced approach for detecting active users in Cognitive Radio Networks – a key step in optimizing communication and reducing interference.

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Quantum GANs for Smarter Network Anomaly Detection

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As modern communication networks grow in complexity, traditional anomaly detection techniques face challenges in identifying sophisticated threats such as distributed denial-of-service (DDoS) or stealth attacks. In our latest study, we explore how quantum machine learning can offer a new frontier for cybersecurity analytics.

In our recent paper, “Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series”

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