We consider the pilot assignment problem in large-scale distributed multi-input multi-output (MIMO) networks, where a large number of remote radio head (RRH) antennas are randomly distributed in a wide area, and jointly serve a relatively smaller number of users (UE) coherently. By artificially imposing structures on the UE-RRH connectivity, we model the network by a partially-connected interference network, so that the pilot assignment problem can be cast as a topological interference management problem with multiple groupcast messages. Building upon such connection, we formulate the topological pilot assignment (TPA) problem in two different ways with respect to whether or not the to-be-estimated channel connectivity pattern is known a priori. When it is known, we formulate the TPA problem as a low-rank matrix completion problem that can be solved by a simple alternating projection algorithm. Otherwise, we formulate it as a sequential maximum weight induced matching problem that can be solved by either a mixed integer linear program or a simple yet efficient greedy algorithm. With respect to two different formulations of the TPA problem, we evaluate the efficiency of the proposed algorithms under the cell-free massive MIMO setting.

中文翻译：

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大规模分布式 MIMO 网络中的拓扑导频分配

我们考虑了大规模分布式多输入多输出 (MIMO) 网络中的导频分配问题，其中大量的远程无线电头端 (RRH) 天线随机分布在广阔的区域中，并共同服务于相对较少数量的用户（UE）连贯。通过人为地对 UE-RRH 连接性强加结构，我们将网络建模为部分连接的干扰网络，从而可以将导频分配问题转换为具有多个组播消息的拓扑干扰管理问题。在这种连接的基础上，我们以两种不同的方式制定拓扑导频分配（TPA）问题，即关于待估计的信道连接模式是否是先验已知的. 当已知时，我们将 TPA 问题表述为可以通过简单的交替投影算法解决的低秩矩阵补全问题。否则，我们将其表述为一个顺序最大权重诱导匹配问题，可以通过混合整数线性程序或简单而有效的贪心算法来解决。针对 TPA 问题的两种不同表述，我们评估了所提出算法在无细胞大规模 MIMO 设置下的效率。