AI 中文总结
研究针对速率分割多址（RSMA）预编码难题，提出循环结构保持图神经网络（RS-GNN），通过构建图特征、闭环干扰感知等实现公共和私有预编码器恢复，解耦参数与系统维度，性能优且可推广，特殊情况也超越现有GNN预编码器。
AI 中文摘要
基于图神经网络（GNN）的预编码在空分多址（SDMA）下的多用户多输入单输出（MU-MISO）系统中，展现出可扩展多用户波束成形的强大潜力。然而，由于速率分割多址（RSMA）固有的公共/私有流结构耦合，直接扩展到RSMA并非易事，这需要根本不同的图表示和置换等变结构。为此，我们提出一种用于可扩展RSMA预编码的循环结构保持图神经网络（RS-GNN）。RS-GNN在每个细化层构建与预编码器相关的图特征，实现闭环干扰感知消息传递，并通过基于解析的基于结构的重建和可微线性求解器恢复公共和私有预编码器。该设计将可学习参数与固定系统维度解耦，无需重新训练即可推广到未见系统规模。我们正式证明RS-GNN在用户和天线排序方面满足混合置换等变性，并表明通过停用公共流分支，RS-GNN可简化为传统SDMA预编码。仿真结果表明，RS-GNN实现了接近WMMSE的和速率性能，在线推理时间显著降低，同时能稳健地推广到未见系统规模；其SDMA特殊情况在未见天线和用户配置、SNR范围及信道分布方面始终优于现有的基于GNN的预编码器。
英文摘要
Graph neural network (GNN)-based precoding has demonstrated strong potential for scalable multi-user beamforming in multi-user multiple-input single-output (MU-MISO) systems under space division multiple access (SDMA). However, direct extension to rate-splitting multiple access (RSMA) is non-trivial due to the coupled common/private-stream structure inherent to RSMA, which requires a fundamentally different graph representation and permutation equivariance structure. Motivated by this, we propose a recurrent structure-preserving graph neural network (RS-GNN) for scalable RSMA precoding. RS-GNN constructs precoder-dependent graph features at every refinement layer, enabling closed-loop interference-aware message passing, and recovers the common and private precoders through an analytically grounded structure-based reconstruction via a differentiable linear solver. This design decouples the learnable parameters from fixed system dimensions, enabling generalization to unseen system sizes without retraining. We formally prove that RS-GNN satisfies mixed permutation equivariance with respect to both user and antenna orderings, and show that RS-GNN reduces to conventional SDMA precoding as a special case by deactivating the common-stream branch. Simulation results demonstrate that RS-GNN achieves near-WMMSE sum-rate performance with significantly lower online inference time, while generalizing robustly to unseen system sizes; its SDMA special case consistently outperforms existing GNN-based precoders across unseen antenna and user configurations, SNR regimes, and channel distributions.
CommentsSubmitted to IEEE Transactions on Wireless Communications
