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 topological 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.
翻译:我们认为大型分布式多投入多产出网络(MIMO)的试点派任问题,在这种网络中,大量的遥控无线电头天线(RRH)天线在广域中随机分布,并一致地为相对较少的用户(UE)服务。我们人为地在UE-RRH连接上强行设置地形结构,从而用部分连接的干扰网络来模拟这个网络,从而可以将试点派任问题作为多组群播送信息的一个地形干扰管理问题。在这种连接的基础上,我们以两种不同的方式提出了地形派任(TPA)问题,即是否预先知道估计的频道连接模式。当知道这个问题时,我们将TPA问题设计成一个低级矩阵完成问题,可以通过简单的交替投影算法加以解决。否则,我们把它设计成一个按顺序最高重量引出匹配的问题,可以通过混合线线程序或简单而有效的贪婪算法来解决。关于TPA问题的两种不同的配制,我们评估无细胞巨型MIMO设置下的拟议算法的效率。