Millimeter Channel Clustering by Self-Organizing Maps With Time-Varying Topological Structure

Cluster-based channel model plays an important role in the fifth-generation (5G) and beyond 5G (B5G) wireless communication systems. A well-designed clustering algorithm can achieve a good compromise between the accuracy and complexity of the wireless channel model. The dependence on prior information of cluster, such as the number of clusters, is a common dilemma of current algorithms. In this article, a self-organizing map with time-varying topological structure (SOM-TVS) clustering algorithm is proposed. First, we combine the locations and weights of the neurons in competition layer and initialize the SOM according to multipath components (MPCs), in which the topology of the SOM is changed with the iterations of the algorithm. Then, a new MPCs’ power weighted Gaussian–Sinc function is proposed to optimize the competitiveness performance of the algorithm. Finally, both the measured and simulated channels in millimeter wave (mmWave) are used to validate the performance of the proposed SOM-TVS algorithm. The SOM-TVS algorithm can perform great effectiveness and robustness based on numerical simulations and experimental validation results. More significantly, the proposed clustering algorithm does not require any prior knowledge of cluster and has a fairly low complexity.