Abstract: A Kernel-power-density based method for wireless channel multipath components (MPCs) clustering. Signals get to the receiver from a transmitter via multipath propagation. MIMO channels can be modeled as double-directional, which contains the information of power, delay, direction of departure (DOD) and direction of arrival (DOA) of MPCs. The MPCs tend to appear in clusters. All the parameters of MPCs can be estimated by using high-resolution algorithms, such as MUSIC, CLEAN, SAGE, and RiMAX. Considering a data snapshot for a certain time with several clusters, which include a number of MPCs, where each MPC is represented by its power, delay, DOD and DOA. This invention adopts a novel clustering framework by using a density based method, which can better identify the local density variations of MPCs and requires no prior knowledge about clusters. It can work for the cluster oriented channel processing technology in future wireless communication field.

Abstract: A Kernel-power-density based method for wireless channel multipath components (MPCs) clustering. Signals get to the receiver from a transmitter via multipath propagation. MIMO channels can be modeled as double-directional, which contains the information of power, delay, direction of departure (DOD) and direction of arrival (DOA) of MPCs. The MPCs tend to appear in clusters. All the parameters of MPCs can be estimated by using high-resolution algorithms, such as MUSIC, CLEAN, SAGE, and RiMAX. Considering a data snapshot for a certain time with several clusters, which include a number of MPCs, where each MPC is represented by its power, delay, DOD and DOA. This invention adopts a novel clustering framework by using a density based method, which can better identify the local density variations of MPCs and requires no prior knowledge about clusters. It can work for the cluster oriented channel processing technology in future wireless communication field.