Method for clustering wireless channel MPCs based on a KPD doctrine

A method for clustering wireless channel and multipath components (MPCs) based on a Kernel Power Density (KPD) Doctrine to make signals get to a receiver from a transmitter via multipath propagation, in which multiple-input-multiple-output (MIMO) channels are modeled as double-directional channels, double-directional pulse response contains data on power, delay, direction of departure (DOD) and direction of arrival (DOA) of multipath component (MPC);

• MPCs of wireless channel tend to appear in clusters, the MPCs in each cluster have similar parameters of power, delay and angle, all MPC parameters are estimated from measurement data by using high-resolution processing procedure;
• a data snapshot is performed with several clusters, each of which has a number of MPCs represented by the power, delay, DOD and DOA,
• characterized in that said method comprises the following steps:
• a) collecting real-time channel data using a multi-antenna channel sounder to continuously acquire raw channel impulse response data and store a raw channel impulse response data in a first storage medium through a First Input First Output (FIFO) controller;
• b) transporting the raw channel impulse response data stored in the first storage medium to a serial-parallel converter, while simultaneously estimating parameters of a baseband response data with multiple parameter estimating processors so as to acquire a corresponding MPC signal for each parallel job;
• then transferring the estimated parameters of the MPC signals to a parallel-serial converter and storing an MPC result in a second storage medium;
• c) using at least eight processors, or different storing areas in one processor, in the multi-antenna channel sounder for a subsequent clustering process in FPGA, in which any data transmission between two adjacent processing units is achieved using shift registers, and all processing units share just a same system clock and perform a parallel process;
• d) transmitting the MPC results stored in the second storage medium into a processing unit 1 of the multi-antenna channel sounder and storing them in a form of a matrix unit;
• e) setting up a counter with an initial value 0 in a processing unit 2, successively searching a nearest neighbor of any MPC x with respect to Euclidean distance in a logic space stored in the processing unit 2, and transmitting it to a processing unit 3 and adding plus one to a counter in processing unit 2;
• f) determining an original KPD of the MPC x according to all MPCs stored in a processing unit 3, a parameter(s) and their statistical distribution characteristics of the x stored in the processing unit 2, and storing the determined KPD in a processing unit 4;
• g) determining a relative KPD of the x in an inner processor based on data stored in the processing unit 3;
• deleting the original KPD of the x previously stored in the processing unit 4, and storing the relative KPD of the x into the processing unit 4;
• h) resetting the counter to zero in the processing unit 2 and repeating steps 5) to 7) until the relative KPD of every MPCs in the processing unit 2 is obtained, and storing all relative KPD data in the processing unit 4;
• i) searching the MPCs with a KPD value equaling to 1 in the processing unit 4, and writing any index of the MPCs with a KPD value equaling to 1 and its corresponding 3D coordinates in the processing unit 2 into a processing unit 5;
• treating these MPCs as the initial centers of MPC clusters in later steps;
• i) searching, in the processing unit 2 of the multi-antenna channel sounder, any MPC which is nearest to an MPC x and whose relative KPD is larger than x, with 3D coordinates and data stored in the processing unit 4, so as to obtain a high-density-neighboring MPC of the x, which is with a logic connection relationship with respect to the nearest MPC x, and whose index is stored in a high-density-neighboring matrix of a processing unit 6;
• k) repeating step h) until all data in the processing unit 2 have been processed, and then storing an index of the high-density-neighboring MPC of the x and an index of logic connection relationship in the high-density-neighboring matrix of the processing unit 6;
• I) inspecting each MPC stored in a memory of a disk of the multi-antenna channel sounder using data retrieval methods, obtaining initial clusters of all MPCs stored in the memory of the disk, thus finishing an initial clustering of all MPCs in the processing unit 2, and storing any cluster index of each MPC into a processing unit 7;
• m) continuously updating the cluster index of each MPC in the processing unit 7 using the data retrieval methods;
• n) counting for different cluster indexes in the processing unit 7, sorting the different cluster indexes, renumbering each cluster index as its rank in a sorted sequence, and storing each continuous index in a processing unit 8;
• and
• o) transmitting the data stored in the multi-antenna channel sounder of the processing unit 8 into a third storage medium, thus completing the clustering process for the MPCs.