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This paper introduces a novel 5G signal processing method that combines a Transformer architecture for parallel computation and a GRU-optimized recurrent architecture to enhance accuracy and efficiency in handling time-varying channel conditions. The Transformer architecture improves computational efficiency, while the GRU layer optimizes convolutional layers for enhanced temporal dynamics capture. Experimental results demonstrate a 5 dB SNR improvement over traditional Decision Feedback Equalizers (DFE) while reducing computational complexity, even under severe inter-symbol interference and non-linear distortions.
By combining Transformers and GRUs, this new 5G signal processing method achieves a 5dB SNR boost over traditional methods while also reducing computational complexity.
With the rapid advancement of 5G technology and the increasing demand for high-performance multimedia applications, efficient and accurate signal processing has become a critical challenge in modern wireless communication systems. This study proposes a novel signal processing method incorporating three key innovations. Firstly, by integrating the Transformer architecture, the model leverages parallel computation capabilities, significantly enhancing computational efficiency compared to traditional sequential approaches, which is particularly advantageous for real-time applications. Secondly, a concise GRU-based recurrent architecture is employed to optimize convolutional layers, thereby improving overall accuracy. The GRU layer effectively captures temporal dynamics, enabling the model to adapt to time-varying channel conditions with greater precision. Thirdly, experimental results demonstrate that the proposed method outperforms existing approaches, including Decision Feedback Equalizers (DFE), in both accuracy and computational efficiency. Specifically, an average improvement of 5 dB in signal-to-noise ratio (SNR) is achieved over traditional DFE-based methods while reducing computational complexity. The improvement remains consistent under severe inter-symbol interference (ISI) and non-linear distortions. These advantages render the proposed model highly suitable for modern wireless communication systems requiring robustness, efficiency, and accuracy. By addressing the limitations of conventional methods, this study provides a reliable and scalable solution for the era of 5G and beyond.