Quantum-Aided Active User Detection for Energy-Efficient CD-NOMA in Cognitive Radio Networks
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.
Their study introduces a Grover-based Quantum Search Algorithm integrated into a Code-Domain Non-Orthogonal Multiple Access (CD-NOMA) framework powered by Wireless Energy Harvesting (WPCN). The algorithm allows a base station to identify active secondary users with far greater computational efficiency than traditional correlation-based methods. By leveraging Grover’s quadratic speedup, the quantum-assisted detector reduces search complexity from O(B) to O(√B), accelerating decision-making and improving network responsiveness. This quantum-assisted detection process significantly enhances network performance while conserving energy, an essential feature for sustainable Cognitive Internet of Things (CIoT) applications.
Figure 3 — Probability of success and energy efficiency versus the SNR at the base station.
Systems with energy harvesting (EH) achieve higher detection success and energy efficiency than those without EH.
(Adapted from “Quantum-Aided Active User Detection for Energy-Efficient CD-NOMA in Cognitive Radio Networks,” IEEE IWCMC 2025.)
Beyond computational gains, the study demonstrates that integrating energy harvesting mechanisms further enhances the sustainability of the system. When secondary users harvest ambient radio frequency energy and use it to transmit data, the overall energy efficiency of the network improves. Simulation results confirm that systems incorporating both quantum detection and energy harvesting achieve higher detection accuracy and better energy utilization, especially in environments with high signal-to-noise ratios.
This research highlights the growing connection between quantum algorithms and next-generation wireless networks, illustrating how quantum search can play a central role in building intelligent, scalable, and energy-efficient communication systems for the CIoTs. As 6G networks move closer to realization, such hybrid quantum-classical models offer a powerful pathway toward more sustainable and adaptive wireless infrastructure.