Shiva Kazemi Taskooh, Mehdi Monemi, Ekram Hossain, Georges Kaddoum, Mehdi Rasti
Jointly supporting communication and distributed sensing over a shared, limited pool of entanglement resources is an attractive prospect for future quantum networks. We develop a network-level quantum integrated sensing and communication (Q-ISAC) framework in which a quantum base station dynamically allocates elementary Bell pairs between communication users and distributed sensing. Sensing resources are adaptively partitioned among multiple GHZ probes with dynamically selected sizes and sensing-node assignments. We derive an effective quantum Fisher information model accounting for probabilistic fusion, transmission loss, and memory decoherence, and formulate a finite-horizon allocation problem subject to communication QoS, expected sensing-information, sensing-reliability, and hardware constraints. A hierarchical constrained proximal policy optimization (HC-PPO) framework combines learned discrete sensing allocation with analytical continuous communication allocation. Numerical results show that GHZ probing requires approximately $27$--30% fewer sensing qubits than separable probing under modest probe-size caps and 65-67% fewer at the highest considered target under the same sensing requirements. Adaptive multi-GHZ partitioning provides greater robustness than single-GHZ sensing, while adaptive communication--sensing sharing outperforms the evaluated fixed splits. The proposed HC-PPO framework satisfies the joint requirements in at least 99.9% of test instances across the considered sensing targets and fusion probabilities. Monte Carlo evaluation separately shows that realized terminal information reached its target in 96-98% of realizations, distinguishing expected-information satisfaction from realized sensing performance.
Abdolrasoul Sakhaei Gharagezlou, Mehdi Monemi, Matti Latva-aho, Mehdi Rasti
Dynamic metasurface antennas (DMAs) have emerged as a promising technology for realizing large-scale antenna arrays in next-generation wireless networks owing to their ability to enable dense and energy-efficient antenna architectures. However, jointly optimizing digital beamforming and DMA element weights under time-varying channel conditions remains challenging due to their coupled effects on the received signal and interference powers. We formulate this problem to maximize time-averaged energy efficiency (EE) under time-varying channel conditions, subject to transmit power constraints, Lorentzian phase limitations, and minimum rate requirements. To enable long-term planning and improve sample efficiency compared with model-free reinforcement learning, we develop a world model framework that learns latent system dynamics from interactions with the environment and jointly optimizes digital beamforming and DMA element weights through imagined trajectories. Furthermore, we incorporate feasibility-preserving action mappings and safety cost signals to ensure constraint satisfaction during policy learning. Simulation results demonstrate that the proposed framework improves the time-averaged EE by approximately 20% compared with the deep Q-network baseline, while achieving higher sample efficiency and more reliable constraint satisfaction.