Identification of Linear bi-dimensional digital modulation schemes via clustering algorithms

This paper presents a software radio based receiver architecture for identification of Linear, bi-dimensional modulation techniques via clustering algorithms. Identification of digital modulation schemes is of great importance in 3G and 4G cellular mobile systems, and it can be well presented as a pattern recognition problem with the use of vector space representation of digitally modulated signals. The proposed system starts with a preprocessing stage that includes: Digital Down Conversion (DDC), symbol rate estimation, base-band filtering, synchronization and normalization. Then the identifier stage follows which uses clustering algorithms along with cluster validity measures if needed to identify the digital modulation scheme used. Three clustering algorithms were compared, K-means clustering algorithm with Dunn index as a validity measure, Fuzzy C-means clustering algorithm with a minimum hard tendency validity measure, and Density Based Clustering. Simulation results for the three approaches are presented under the presence of AWGN.