Dynamic Hybrid Resource Utilisation and MCS-based Intelligent Layering

The coexistence of heterogeneous service classes in 5G Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and Massive Machine-Type Communication (mMTC) poses major challenges for meeting diverse Quality-of-Service (QoS) requirements under limited spectrum and power resources. Existing radio access network (RAN) slicing schemes typically optimise isolated layers or objectives, lacking physical-layer realism, slot-level adaptability, and interpretable per-slice performance metrics. This paper presents a joint optimisation framework that integrates Dynamic Hybrid Resource Utilisation with MCS-Based Intelligent Layering, formulated as a mixed-integer linear program (MILP) that jointly allocates bandwidth, power, and modulation and coding scheme (MCS) indices per slice. The model incorporates finite blocklength effects, channel misreporting, and correlated fading to ensure realistic operation. Two modes are implemented: a Baseline Mode that ensures resource-efficient QoS feasibility, and an Ideal-Chaser Mode that minimises deviation from ideal per-slice rates. Simulation results show that the proposed approach achieves energy efficiencies above $10^7$~kb/J in Baseline Mode and sub-millisecond latency with near-ideal throughput in Ideal-Chaser Mode, outperforming recent optimisation and learning-based methods in delay, fairness, and reliability. The framework provides a unified, interpretable, and computationally tractable solution for dynamic cross-layer resource management in 5G and beyond networks.

Adaptive Entanglement-Aware Routing for Satellite Quantum Networks under Orbital and Atmospheric Variability

The expansion of satellite-based quantum networks requires adaptive routing mechanisms that can sustain entanglement under dynamic orbital and atmospheric conditions. Conventional schemes, often tailored to static or idealised topologies, fail to capture the combined effects of orbital motion, fading, and trust variability in inter-satellite links. This work proposes an \textit{adaptive entanglement-aware routing framework} that jointly accounts for orbital geometry, atmospheric attenuation, and multi-parameter link evaluation. The routing metric integrates fidelity, trust, and key-rate weights to maintain connectivity and mitigate loss from turbulence and fading. Monte Carlo simulations across multiple orbital densities ($ρ= 10^{-6}$~km$^{-3}$) and environmental regimes, standard atmosphere, strong turbulence, and clear-sky LEO show up to a 275\% improvement in key generation rate and a 15\% increase in effective entanglement fidelity over existing adaptive methods. The framework achieves sub-linear path-length scaling with network size and remains robust for fading variances up to $σ_{\mathrm{fade}}=0.1$, demonstrating strong potential for future global quantum constellations.