Deep Learning-Driven CNN-Transformer-GRU Hybrid Architecture for Complex Channel Equalization

With the technological evolution from 5G to 6G, digital communication systems are developing towards higher frequency bands and broader bandwidths. Channel distortion and inter-symbol interference have become key issues constraining communication quality. Channel equalization is the core link to overcome multipath fading and inter-symbol interference. However, traditional channel equalization techniques face bottlenecks such as low efficiency and poor performance in complex time-varying channels, multipath time delay spread, and dynamic interference. In recent years, deep learning has provided breakthrough ideas for channel equalization. This paper introduces a deep learning channel equalization model architecture that integrates CNN, Transformer, and GRU, primarily addressing channel distortion and inter-symbol interference in complex communication environments. Through experiments, it has been verified that the model exhibits significant advantages in key indicators such as Mean Squared Error (MSE) and Symbol Error Rate (SER), effectively enhancing the performance of communication systems in complex scenarios with Gaussian White noise and multipath fading. In the future, the integration of deep learning with traditional channel equalization techniques will drive the development of digital communication systems, paving new technological paths for intelligent signal processing.