Channel Scenario Identification Under Varying SNR Conditions Using Transfer Learning

Identifying channel scenarios is crucial for developing and optimizing wireless communication systems in the forthcoming 6G networks. In these networks, transceivers can adjust transmission schemes and optimize resource allocation based on the identified channel scenario. This study proposes a deep convolutional neural network (CNN) model to identify different channel scenarios using channel frequency response (CFR) autocorrelation. The model is trained to recognize the unique features of various channel scenarios and demonstrates strong performance under varying signal-to-noise ratio (SNR) conditions. Additionally, transfer learning is employed to improve the model's ability to generalize to a broader range of SNR conditions beyond those used in training. With minimal data, the final two layers of the CNN model can be adjusted to adapt to new SNR conditions without requiring retraining from the beginning. The proposed deep CNN, combined with transfer learning, can effectively generalize its strong performance from known SNR conditions to new ones.