Maha Mubarak

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

Insights /

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.

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

Quantum GANs for Smarter Network Anomaly Detection

Insights /

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”

Quantum GANs for Smarter Network Anomaly Detection Read More »

Congratulations to Prof. Soumaya and Deemah on their new IWCMC​ 2025 publication

News /

We extend our warmest congratulations to Prof. Soumaya Cherkaoui and Deemah H. Tashman, PhD fellow at LincsLab, on their recent publication titled:
“Quantum-Aided Active User Detection for Energy-Efficient CD-NOMA in Cognitive Radio Networks”, presented at the 2025 International Wireless Communications and Mobile Computing Conference (IWCMC) and published by IEEE.

Congratulations to Prof. Soumaya and Deemah on their new IWCMC​ 2025 publication Read More »

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