Search for: All records

This paper concerns the task of generating simpler yet accurate mmWave channel models based on clustering all multipath components arriving at the receiver. Our work focuses on 28 GHz communications in urban outdoor scenarios simulated with a ray-tracer tool. We investigate the effectiveness of k- means and k-power-means clustering algorithms in predicting the optimal number of clusters by using cluster validity indices (CVIs) and score fusion techniques. Our results show how the joint use of these techniques generate accurate approximation of the mmWave large-scale and small-scale channel models, greatly simplifying the complexity of analyzing large amount of rays at any receiver location. 

Radio channel propagation models for the millimeter wave (mmWave) spectrum are extremely important for planning future 5G wireless communication systems. Transmitted radio signals are received as clusters of multipath rays. Identifying these clusters provides better spatial and temporal characteristics of the mmWave channel. This paper deals with the clustering process and its validation across a wide range of frequencies in the mmWave spectrum below 100 GHz. By way of simulations, we show that in outdoor communication scenarios clustering of received rays is influenced by the frequency of the transmitted signal. This demonstrates the sparse characteristic of the mmWave spectrum (i.e., we obtain a lower number of rays at the receiver for the same urban scenario). We use the well-known k-means clustering algorithm to group arriving rays at the receiver. The accuracy of this partitioning is studied with both cluster validity indices (CVIs) and score fusion techniques. Finally, we analyze how the clustering solution changes with narrower-beam antennas, and we provide a comparison of the cluster characteristics for different types of antennas.