Deep Learning-based Automatic Modulation Recognition Method in the
Presence of Phase Offset
Abstract
Automatic modulation recognition (AMR) plays an important role in
various communications systems. It has the ability of adaptive
modulation and can adapt to various complex environments. Automatic
modulation recognition is also widely used in orthogonal frequency
division multiplexing (OFDM) systems. However, because the recognition
accuracy of traditional methods to extract the features of OFDM signals
is very limited. In order to solve these problems, many deep learning
based AMR methods have been proposed to improve the recognition
performance. However, most of these AMR methods neglect the harmful
effect by carrier phase offset (PO) which often appears in real
communications systems. Hence it is required to consider the PO effect
for designing the OFDM system. Unlike conventional methods, we propose a
convolutional neural network (CNN) based AMR method for considering PO
in the OFDM system. The proposed method is used to eliminate the PO to
achieve the high classification accuracy. Experiment results are
provided to confirm the proposed method when comparing to conventional
methods.