S. Chatzinotas

Universal Intelligent Small Cell for Next Generation Cellular Networks

Stacked Intelligent Metasurfaces Assisted UAV Communications

Numerical results demonstrate that the proposed SIM-assisted architecture significantly improves spectral efficiency while maintaining low hardware complexity, and highlight the impact of the number of metasurface layers and size of each layer on system performance.

Quantum Decision Theory for Displacement Detection with Finite-Energy GKP States

We develop a quantum-decision-theoretic framework for detecting phase-space displacements with finite-energy, $d$-level Gottesman-Kitaev-Preskill (GKP) probes. For single-mode and entanglement-assisted architectures, we derive the Bayesian minimum-error probability, the optimal Neyman-Pearson receiver-operating charact...

Selective Handover and SLA-Aware Resource Allocation for Diverse Services in a LEO Satellite Network

A selective handover strategy is proposed where the path recomputation and VNF remapping are done only if there is a change in the previous routing path, which ensures that only critical handovers are carried out while discouraging unnecessary reconfigurations, which result in service discontinuity.

Quantum Reservoir Computing: Recent Advances and Future Directions

A common system model is developed that connects QRC foundations, computational properties, reservoir architectures, operating protocols, and physical implementations and specifies the resource accounting, benchmark standards, and theoretical criteria needed to evaluate claims of quantum advantage.

Energy Efficient Multi-User Beamforming and 3D Position Optimization for SIM-Assisted UAVs

This paper studies energy-efficient downlink multi-user transmissions with unmanned aerial vehicle (UAV) communication systems equipped with stacked intelligent metasurfaces (SIM), enabling wave-domain analog beamforming through multiple cascaded metasurface layers, while low-dimensional digital precoding is carried ou...

Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networks

A two-timescale multi-layer deep reinforcement learning framework with a latent action space (2T-MDRL-LA) to jointly optimize service placement, user association, computational delegation, task offloading, and user transmit power and achieves near-optimal performance compared to branch-and-bound solutions.