提出新型图神经网络，让无线系统更智能地分配用户连接
Association-Aware GNN for Precoder Learning in Cell-Free Systems
- 用图神经网络显式建模用户与基站的动态连接关系
- 在多种场景下表现优于传统方法，且训练推理开销低
- 适合研究大规模无线网络优化的研究者和工程师
深度学习已被广泛认为是优化传统蜂窝系统中多用户多天线预编码器的有前途方法。然而，无蜂窝系统与蜂窝系统的关键区别在于用户设备（UE）与接入点（AP）之间连接关系的灵活性。因此，最优预编码器不仅依赖于信道状态信息，还取决于动态的用户-接入点关联状态。本文提出一种关联感知图神经网络（AAGNN），将关联状态显式融入预编码设计中。利用无蜂窝预编码策略的置换等变性特性，降低AAGNN的训练复杂度，并引入注意力机制提升其泛化能力。仿真结果表明，所提AAGNN在学习性能和泛化能力方面均优于基线学习方法，同时保持低训练与推理复杂度。
原文摘要 · Abstract (English)
Deep learning has been widely recognized as a promising approach for optimizing multi-user multi-antenna precoders in traditional cellular systems. However, a critical distinction between cell-free and cellular systems lies in the flexibility of user equipment (UE)-access point (AP) associations. Consequently, the optimal precoder depends not only on channel state information but also on the dynamic UE-AP association status. In this paper, we propose an association-aware graph neural network (AAGNN) that explicitly incorporates association status into the precoding design. We leverage the permutation equivariance properties of the cell-free precoding policy to reduce the training complexity of AAGNN and employ an attention mechanism to enhance its generalization performance. Simulation results demonstrate that the proposed AAGNN outperforms baseline learning methods in both learning performance and generalization capabilities while maintaining low training and inference complexity.
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