Graph neural network-based end-to-end resource allocation for energy-efficient RIS-UAV-NOMA-THz networks

Terahertz (THz) access provides multi-GHz bandwidth for sixth-generation (6G) services, but its short-range propagation, molecular absorption, blockage, and narrow-beam misalignment make end-to-end (E2E) delay-energy guarantees difficult when computation tasks are offloaded to an edge computing node (ECN). When THz access is combined with non-orthogonal multiple access (NOMA), feasible successive interference cancellation (SIC) ordering must also be maintained, further coupling radio decisions with queueing and computation constraints. A reconfigurable intelligent surface (RIS) mounted on an unmanned aerial vehicle (UAV) create a controllable reflected path to mitigate blockage, yet practical deployment introduces b-bit phase quantization, UAV kinematics, and battery limitations that invalidate many idealized control assumptions. This paper proposes a constraint-aware E2E resource allocation framework for RIS-UAV-assisted THz NOMA-MEC networks. We formulate a single optimization problem that decides users’ transmit powers, SIC order, b-bit RIS phase shifts, UAV 3D waypoints, task offloading ratios, and per-user edge CPU allocations, subject to spectral masks, power limits, queue stability, CPU budgets, UAV kinematics/battery evolution, and RIS quantization constraints. To handle the high-dimensional coupled state, we represent the system as a heterogeneous graph (users, RIS-UAV, and ECN) and learn a graph neural network (GNN) policy. A differentiable feasibility projection enforces hard constraints, while a weighted time-energy objective (uplink transmission, edge execution, downlink return, and UAV propulsion) is augmented with a CVaR term to control tail latency. Simulations show reduced latency, lower energy per bit and energy-delay product, and improved spectral efficiency and outage performance compared with DRL/MARL and greedy baselines, with gains increasing for larger RIS apertures and finer phase resolution.