PCGFormer: Power Control for Device-to-Device Communications With Graph Transformer

Power control in Device-to-Device networks is still a challenging learn-to-optimize problem, although recent AI-empowered approaches (e.g., PCGNN/GRLinQ/UFPLinQ) have made remarkable progress regarding effectiveness and in-domain generalization. There is still some concern about whether or not purely data-driven approaches are generalizable across domains with diverse interference patterns. To clear such a concern, we propose a powerful data-driven approach, PCGFormer, enabled by a novel graph transformer architecture with fast attention mechanisms, answering the question in the affirmative. By extensive experiments in both strong and weak interference regimes, PCGFormer exhibits excellent sum rate performance comparable to the state-of-the-art model-based/driven methods, e.g., FPLinQ and UFPLinQ, with substantially reduced computational complexity. More impressively, PCGFormer demonstrates excellent cross-domain generalization across diverse interference patterns – scaling law still rocks – breaking through a key bottleneck of small-scale data-driven approaches, e.g, PCGNN. These findings establish PCGFormer as a practical and powerful candidate for large-scale wireless resource allocation.