Noise-Adaptive Auto-Encoder for Modulation Recognition of RF Signal

This paper presents an efficient Deep Neural Network (DNN) design optimized for the modulation classification of the received Radio Frequency (RF) signal. Considering that the transmitted signals are exposed to various noise sources through the transmission channel, we propose an adaptive auto-encoder mechanism to suppress the noise efficiently. The proposed auto-encoder enables the adaptive characteristics by adopting the additional parameters to make a balance between the skip connection and the compression/decompression process. The results show that the proposed adaptive auto-encoder can improve the classification accuracy especially in low signal-to-noise ratio (SNR) area. The impact due to the additional network required to generate the balancing parameters on the hardware design is minimized by sharing the data compression process that already exists in the auto-encoder.