In this paper, we investigate an unmanned aerial vehicle (UAV) communication system assisted by stacked intelligent metasurfaces (SIMs), which enable programmable wave-domain signal processing through multiple cascaded metasurface layers. By shifting part of the beamforming functionality from the RF/digital domain to the electromagnetic domain, SIMs allow the realization of energy-efficient hybrid beamforming architectures suitable for aerial platforms. We formulate the joint design of digital precoding, SIM phase configuration, and UAV positioning for multi-user downlink sum-rate maximization. To solve the resulting non-convex problem, we develop an alternating optimization framework that guarantees monotonic improvement of the objective. 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.
C. K. Sheemar, Giovanni Iacovelli, Sourabh Solanki et al.· 0 citations
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 characteristic, and the corresponding minimum detectable displacement. Finite-energy effects are treated through exact theta-series displacement kernels, while pure loss followed by quantum-limited amplification is mapped to an effective Gaussian random-displacement channel. Entanglement removes preparation-dependent blind directions and preserves both logical displacement labels, although it does not surpass the pointwise optimized single-mode strategy in the noiseless pure-state setting. We benchmark the resulting protocols against coherent-state, direction-matched squeezed-vacuum, and twin-beam schemes at equal nominal squeezing. Numerical results identify finite-squeezing and lossy regimes in which GKP probes achieve both a lower Bayesian error and a smaller minimum detectable perturbation than the selected Gaussian receivers.
The integrated satellite-terrestrial networks (STNs) aim to provide ubiquitous connectivity and support various services with diverse requirements. Each service request has to go through a sequence of virtual network functions (VNFs) that should be mapped on its routing path. The STNs are equipped with limited communication and computation resources, making it challenging to enable heterogeneous services. Furthermore, the movement of satellites causes frequent changes in topology, which can impact the continuity of long-lasting requests. For requests lasting multiple time frames, the VNF mapping update and path recomputation at the beginning of each time frame is computationally expensive and can cause unwanted service interruptions. Therefore, we propose a selective handover strategy where the path recomputation and VNF remapping are done only if there is a change in the previous routing path. The selective handover strategy ensures that only critical handovers are carried out while discouraging unnecessary reconfigurations, which result in service discontinuity. We develop a software-defined networking (SDN) based experimental testbed that allows us to realistically consider the system constraints. The VNF mapping and path computation for a request are done in a proactive manner, and the rate meters are installed on the switches according to the current network traffic to efficiently utilize the available bandwidth. The simulation results certify that the proposed strategy reduces the packet loss by up to 11.5% and 18.5% as compared to the benchmark schemes and provides stable throughput for eMBB services with minimal service-level agreement (SLA) violations, while also ensuring the latency requirements of mMTC.
Muhammad Ahsan, T. Vu, Ilora Maity et al.· International Mediterranean...· 0 citations
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.
Shehbaz Tariq, M. Talha, Arshid Ali et al.· 0 citations
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 out using a limited number of transmit radio-frequency chains. This architecture enables flexible electromagnetic wave manipulation with reduced hardware complexity, making it particularly suitable for energy-constrained aerial platforms. We formulate a hardware-aware energy-efficiency (EE) maximization problem aiming to jointly optimize the digital precoder, the phase shifts of all SIM layers, and the three-dimensional UAV position under transmit-power, SIM operation, and UAV deployment constraints. The resulting problem is highly non-convex due to the fractional objective, the cascaded SIM structure and the unit-modulus phase constraints of the constituent metasurface layers, as well as the non-linear UAV-dependent channel. To address these challenges, we develop a transform-based alternating optimization framework that combines Dinkelbach's method, dual and quadratic transforms, to enable closed-form digital beamforming, Riemannian manifold optimization for SIM phase shifts, and successive convex approximation (SCA) for UAV positioning. Convergence and complexity analyses are provided to characterize the proposed algorithm. The presented numerical results showcase that the proposed joint design significantly improves EE compared with fully digital and maximum ratio transmission benchmark schemes, while revealing important design trade-offs among transmit power, SIM size, and the number of its constituent stacked layers.
C. K. Sheemar, Giovanni Iacovelli, Sourabh Solanki et al.· 0 citations
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.
V. Son, Van-Dinh Nguyen, Ngoc Hung Nguyen et al.· 0 citations