This paper considers the blind detection of spatial modulation systems multiple-input multiple-output systems. In this system, we propose a clustering detection framework with graph theory that conducts signal detection without the training of channel state information. In detail, firstly, by dynamically controlling the size of each cluster, we transform the original optimization problem of the traditional K-means clustering detector into a new optimization problem. In addition, the cluster assignment subproblem of the iterative clustering algorithm for solving the new optimization problem makes it equivalent to a minimum cost flow linear network optimization problem of graph theory, which can be addressed by the breadth-first algorithm. Moreover, a novel clustering detector with the breadth-first algorithm is presented correspondingly. Numerical results show that the proposed detector is efficient in avoiding the undesired local optima and can closely approach the performance of the optimal detector.

本文考虑了空间调制系统多输入多输出系统的盲检测。在该系统中，我们提出了一种基于图论的聚类检测框架，该框架无需进行信道状态信息的训练即可进行信号检测。详细地说，首先，通过动态控制每个聚类的大小，我们将传统的K-均值聚类检测器的原始优化问题转化为新的优化问题。另外，用于解决新的优化问题的迭代聚类算法的聚类分配子问题使其等效于图论的最小成本流线性网络优化问题，可以通过广度优先算法解决。此外，提出了一种具有广度优先算法的新型聚类检测器。