Score-Guided Generative Diffusion for Energy-Efficient Resource Allocation in Multi-Cell 6G Edge Networks

Abstract

Learned resource allocators for sixth-generation edge networks usually treat operational limits as penalties and may return infeasible decisions. This paper studies energy minimization in multi-cell uplink non-orthogonal multiple access mobile edge computing networks. Treating inter-cell interference as a receiver-side quantity makes the two-user cluster sum rate depend only on the aggregate received power of the pair. Joint optimization of user association, subchannel assignment, transmit power and computation allocation is formulated as a mixed-integer nonlinear program with mandatory service and solved by a safeguarded alternating scheme: a capacity-aware heuristic proposes an association, a conditional diffusion model generates powers through a bounded latent mapping that enforces the power limits by construction, and the computation allocation follows in closed form. Given a feasible initialization, accepting only complete candidate tuples that pass the feasibility and energy tests preserves feasibility and yields a non-increasing objective without requiring block-wise optimality. In simulations, total energy is about 10% lower than with a deep reinforcement-learning baseline and 16% lower than with successive convex approximation, mainly through lower transmission energy (0.98 J vs. 1.32 J). At the default configuration, fewer than 2% of generated candidates fail the terminal test, and one outer iteration takes 3.52 s at the largest tested network, compared with 72.5 s for successive convex approximation.