Efficient Automatic Modulation Classification for Next Generation Wireless Networks

This paper introduces a novel automatic modulation classification (AMC) algorithm for wireless communication systems. With the advent of sixth-generation (6G) networks, the demand for high-accuracy and computationally-efficient AMC algorithms has become increasingly pressing. In response to this need, we propose a new model called the threshold denoise recurrent neural network (TDRNN). The TDRNN combines a threshold denoise (TD) module and a recurrent neural network (RNN) module to achieve high accuracy and fast computation times. The TD module reduces the received signal's noise level, while the RNN module performs the modulation classification on the denoised signal. The proposed TDRNN algorithm is evaluated on various modulation schemes and signal-to-noise ratios (SNR). The experimental results demonstrate that the TDRNN algorithm outperforms existing methods in terms of accuracy, speed, and computational complexity. The TDRNN algorithm is suitable for adaptive coding and modulation in 6G wireless communication systems.