  Automatic modulation recognition (AMR) is a promising technology for
intelligent communication receivers to detect signal modulation schemes.
Recently, the emerging deep learning (DL) research has facilitated
high-performance DL-AMR approaches. However, most DL-AMR models only focus on
recognition accuracy, leading to huge model sizes and high computational
complexity, while some lightweight and low-complexity models struggle to meet
the accuracy requirements. This letter proposes an efficient DL-AMR model based
on phase parameter estimation and transformation, with convolutional neural
network (CNN) and gated recurrent unit (GRU) as the feature extraction layers,
which can achieve high recognition accuracy equivalent to the existing
state-of-the-art models but reduces more than a third of the volume of their
parameters. Meanwhile, our model is more competitive in training time and test
time than the benchmark models with similar recognition accuracy. Moreover, we
further propose to compress our model by pruning, which maintains the
recognition accuracy higher than 90% while has less than 1/8 of the number of
parameters comparing with state-of-the-art models.
