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
Quantum reservoir computing (QRC) uses the dynamics of a fixed or weakly tuned quantum system to transform temporal and sequential inputs into measured features, while training is typically confined to a classical readout. This separation reduces reliance on repeated quantum parameter updates and avoids the barren plateaus associated with variational circuit training. Its computational power is often attributed to the exponentially large Hilbert space of the quantum system. However, the memory, nonlinearity, and expressivity that determine what a reservoir can actually compute depend jointly on the input encoding, quantum evolution, observables, measurement, and readout, not on Hilbert space dimension alone. On hardware, these capabilities are further constrained by finite sampling, hardware noise, measurement backaction, and the cost of estimating observables, so a large state space alone does not guarantee useful computation. In this survey, we develop a common system model that connects these components and use it to organize QRC foundations, computational properties, reservoir architectures, operating protocols, and physical implementations. We examine spin, photonic, superconducting, bosonic, neutral atom, and other analog platforms, together with applications, software and high performance computing support, benchmarking, and reproducibility. The analysis distinguishes hardware demonstrations from simulations and identifies the assumptions and resources that govern comparisons across implementations. Current results do not establish a broad quantum advantage over well matched classical reservoirs. We therefore specify 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