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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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Photo of D-Wave quantum computer equipped with a quantum annealing processor..

Quantum optimization shows potential as a powerful tool for enhancing resource allocation efficiency in Open Radio Access systems (Open RAN)

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Open Radio Access Networks (Open RAN) are emerging as a key architectural approach in next-generation mobile networks. One of its key capabilities—RAN slicing—enables multiple virtual networks to operate on a shared radio infrastructure.

Quantum optimization shows potential as a powerful tool for enhancing resource allocation efficiency in Open Radio Access systems (Open RAN) Read More »

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