arXiv subjects

Noha Hassan

Publications and source records attributed to Noha Hassan.

3 recordsLinked to original sources

arXiv2026

Path-Based Quantum Meta-Learning for Adaptive Optimization of Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surfaces (RISs) modify signal reflections to enhance wireless communication capabilities. Classical RIS phase optimization is highly non convex and challenging in dynamic environments due to high interference and user mobility. Here we propose a hierarchical multi-objective quantum metalearning algorithm that switches among specific quantum paths based on historical success, energy cost, and current data rate. Candidate RIS control directions are arranged as switch paths between quantum neural network layers to minimize inference, and a scoring mechanism selects the top performing paths per layer. Instead of merely storing past successful settings of the RIS and picking the closest match when a new problem is encountered, the algorithm learns how to select and recombine the best parts of different solutions to solve new scenarios. In our model, high-dimensional RIS scenario features are compressed into a quantum state using the tensor product, then superimposed during quantum path selection, significantly improving quantum computational advantage. Results demonstrate efficient performance with enhanced spectral efficiency, convergence rate, and adaptability.

eess.SY↗

arXiv2026

Quantum Graph Neural Networks for Double-Sided Reconfigurable Intelligent Surface Optimization

As a key enabler for sixth-generation (6G) wireless communications, reconfigurable intelligent surfaces (RISs) provide the flexibility to control signal strength. Nevertheless, optimizing hundreds of elements is computationally expensive. To overcome this challenge, we present a quantum framework (QGCN) to jointly optimize the physical and electromagnetic response of a double-sided RIS design that incorporates discrete phase shifts and inter-element coupling. The core contribution is the adaptive activation or deactivation of elements, allowing a virtual spacing mechanism using PIN diode switches. We then solve a multi-objective problem that maximizes the minimum user data rate subject to constraints on aperture length and mutual coupling between active elements. Experimental results on IBM Quantum's 127-qubit ibm_kyiv superconducting processor demonstrate that the proposed QGCN algorithm reduces both per-iteration computational complexity and memory requirements compared to existing approaches. Also, the QGCN outperforms classical graph neural networks (GNN) on an equivalent graph topology by an additional $+$0.38 bps/Hz. This advantage is increasing with increasing array sizes.

eess.SY↗

arXiv2020

User Association in Coexisting RF and TeraHertz Networks in 6G

While fifth generation (5G) networks are ready for deployment, discussions over sixth generation (6G) networks are down the road. Since high frequencies like terahertz (THz) will be central to 6G, in this paper, we propose two user association (UE) algorithms considering a coexisting RF and THz network that balances the traffic load across the network by minimizing the standard deviation of the network traffic load. Our algorithms capture the heterogeneity observed at RF and THz frequencies such as transmission bandwidth, molecular absorption, transmit powers, etc. Unlike typical unsupervised clustering algorithms (e.g. k-means, k-medoid, etc.) that search for appropriate cluster centers' locations, our algorithms identify the appropriate UEs to be associated to a certain BS such that the overall network load standard deviation (STD) can be minimized subject to users' rate constraints. In particular, our algorithms cluster UEs to every base station (BS) such that the traffic load across the network can be balanced, i.e., by minimizing the STD of network traffic load. Numerical results show that the proposed algorithms outperform the classical user association algorithms in terms of data rate, traffic load balancing, and user's fairness.

cs.IT↗