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A multi-cell cluster-free NOMA framework is proposed, where both intra-celland inter-cell interference are jointly mitigated via flexible cluster-freesuccessive interference cancellation (SIC) and coordinated beamforming design.The joint design problem is formulated to maximize the system sum rate whilesatisfying the SIC decoding requirements and users' minimum data raterequirements. To address this highly complex and coupling non-convex mixedinteger nonlinear programming (MINLP), a novel distributed auto-learning graphneural network (AutoGNN) architecture is proposed to alleviate the overwhelminginformation exchange burdens among base stations (BSs). The proposed AutoGNNcan train the GNN model weights whilst automatically optimizing the GNNarchitecture, namely the GNN network depth and message embedding sizes, toachieve communication-efficient distributed scheduling. Based on the proposedarchitecture, a bi-level AutoGNN learning algorithm is further developed toefficiently approximate the hypergradient in model training. It istheoretically proved that the proposed bi-level AutoGNN learning algorithm canconverge to a stationary point. Numerical results reveal that: 1) the proposedcluster-free NOMA framework outperforms the conventional cluster-based NOMAframework in the multi-cell scenario; and 2) the proposed AutoGNN architecturesignificantly reduces the computation and communication overheads compared tothe conventional convex optimization-based methods and the conventional GNNswith fixed architectures.

Distributed Auto-Learning GNN for Multi-Cell Cluster-Free NOMA Communications