Alan A. Pennacchio, João Paulo C. L. da Costa, Vítor M. Bordini, Eigenfilter-based Automatic Modulation Classification with Offsets for Distributed Antenna Systems Alan A. Pennacchio, João Paulo C. L. da Costa, Vítor M. Bordini, Giovanni del Galdo and Samuel M. L. da Silva 1-Objectives 4-Proposed Eingenfilter-based AMC with offsets To propose na automatic modulation classification (AMC) with offsets; Incorporate the proposed approach to Distributed Antenna Systems by applying the eingenfilter. - The proposed scheme is composed of three main steps: Step 1) the eingenfilter is applied to obtein the estimated signal. where is the eigenvector of the sample covariance matrix of corresponding to the greatest eigenvalue. By applying the eigenfilter, the SNR is maximized. Step 2) we extract the features given by the cumulants of the input signal . where: and are insentitive to offsets and, therefore, are traditionally used to offset detection. However, in order to take into account the other cumulants, we propose the trainning of the neural network by varying both offsets and modulation schemes. Step 3) The proposed trained Artificial Neural Network (ANN) returns the modulation. 2-Introduction In military applications, AMC can be applied to recover messages from intercepted signals. In civilian applications, AMC can be applied for cognitive radios that opportunistically exploits the idle spectrum and that can be included in 5G systems. Distributed antenna systems: improved signal detection, demodulation and recovery 5-Simulation results Four types of modulations: BPSK, QPSK, 8PSK and 16QAM For each modulation: 10000 signals with 1024 samples Proposed scheme with seven cumulants (7C) due to the proposed ANN trainning Eigenfilter wth four antennas 3-Data Model -The received signal can be modeled as: where: : : channel vector with amplitudes and phases offsests of the transmitted signals; : the transmitted symbols : the additive white Gaussian noise vector 6-References O. Dobre, A. Abdi, Y. Bar-ness and W. Su. ``Survey of automatic mudulation classification techniques:classical approaches and new trends". Communications, IET, vol. 1, no. 2, pp. 137-156, April 2007. S. Sobolewski, W. Adams, and R. Sankar, ``Universal nonhierachical automatic modulation recognition techniques for distinguishing bandpass modulated waveforms based on signal estatistics, cumulant, cyclostationary, multifractal and Fourier-wavelet transform features', in Military Communication Conference (MILCOM), 2014 IEEE, October 2014, pp. 748-753. V. Orlic and M. Duckic, ``Automatic modulation classification algorithms using higher-order cumulants under real world-channel conditions", Communications Letters, IEEE, vol. 13, no. 12, pp. 917-919, December 2009.