Transformer based HF communication demodulation

In low signal-to-noise ratio (SNR) environments, shortwave channels are subject to impairments such as multipath effects, frequency-selective fading, and time-selective fading. These significantly increase the bit error rate (BER), adversely affecting communication quality and making it difficult to meet communication requirements. Addressing the limitations of traditional techniques like the Least Squares (LS) and Minimum Mean Square Error (MMSE) methods, this paper proposes a deep learning-based demodulation algorithm for shortwave channels. The algorithm employs a multi-channel approach, feeding the real and imaginary parts of the signal as separate input channels to enhance demodulation accuracy. It integrates a convolutional neural network (CNN) module for local feature extraction, aiming to more effectively capture the signal's local characteristics. Simulation results demonstrate that within the SNR range of -10 dB to 10 dB, the deep learning approach significantly outperforms traditional methods in demodulation performance. Compared to benchmark methods including LS channel estimation, MMSE channel estimation, and other networks such as CNN, GRU, and CNN-RNN, the proposed Transformer-based network demodulation method consistently achieves SNR gains of 1-5 dB. The simulations prove that the proposed Transformer-based demodulation receiver exhibits superior performance under low-SNR, high-frequency channel conditions compared to both traditional methods and existing deep learning models. These findings offer valuable insights for enhancing the reliability and stability of shortwave communication systems.