Error Correction Coding for One-Bit Quantization With CNN-Based AutoEncoder
Rui Zeng, Zhilin Lu, Jintao Wang, Jian Song · IEEE Communications Letters · 2022
Recently, a deep learning based error correction coding scheme is proposed to compensate for the severe distortion due to one-bit quantization. However, the fully-connected (FC) layers aided autoencoder is too heavy, resulting in high storage cost. In this letter, a novel convolutional autoencoder named ECCNet is introduced to lighten the scheme. Additionally, the soft quantization function is introduced to overcome the gradient mismatch. The squeeze and excitation (SE) block is applied for further performance boosting. Simulations show that the BER performance of the proposed ECCNet outperforms the previous state-of-the-art method under 16-QAM modulation with fewer parameters required. Furthermore, the proposed autoencoder design has impressive robustness in near Gaussian multi-path fading channels.