A clustering detector with graph theory for blind detection of spatial modulation systems

초록

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.

키워드