A graph neural network (GNN) based access point (AP) selection algorithm for cell-free massive multiple-input multiple-output (MIMO) is proposed. Two graphs, a homogeneous graph which includes only AP nodes representing the structure of the APs in the network, and a heterogeneous graph which includes both the AP nodes and user equipment (UE) nodes are constructed to represent a cell-free massive MIMO network. A GNN based on the inductive graph learning framework GraphSAGE is used to obtain the embeddings which are then used to predict the links between the nodes. Numerical results show that compared to proximity-based AP selection algorithms, the proposed GNN based algorithm predicts more potential links with a limited number of reference signal receive power (RSRP) measurements. Unlike the other AP selection algorithms in the literature, the proposed algorithm does not assume the knowledge of RSRP measurements of every AP-UE combination for optimal AP selection. Furthermore, the proposed algorithm is scalable in terms of the number of users in the cell-free system.

翻译：提出了两张图表,其中仅包括反映网络中AP结构的AP节点,而包括AP节点和用户设备(UE)的多元图,以构建一个无细胞大型MIMO网络。基于输入式图解学习框架的GogSAGE用来获取嵌入器,然后用来预测节点之间的联系。数字结果显示,与近距离AP选择算法相比,提议的GNN算法预测了与数量有限的参考信号接收功率测量的更多潜在链接。与文献中的其他AP选择算法不同,拟议的算法并不假定对最佳AP-UE组合进行RSRP测量的知识。此外,拟议的算法从无细胞系统中用户人数的角度来看是可扩缩的。