In recent years, data-driven machine learning approaches have been explored to enhance traditional model-based processing in digital communication systems. The focus is on proposing novel neural network (NN-)based equalization methods, specifically tailored for single carrier frequency domain equalization (SC-FDE) systems. SICNNv1 and SICNNv2 are designed by deep unfolding a model-based iterative soft interference cancellation method to address the limitations of model-based approaches. These NN-based equalizers aim to provide superior performance with reduced computational complexity compared to existing methods.
The study compares the proposed NN-based equalizers with state-of-the-art models and highlights their advantages in achieving better bit error ratio performance. By generating training datasets for NN-based equalizers, the performance at high signal-to-noise ratios is significantly improved. The paper also delves into the structure of SICNNv1 and SICNNv2, showcasing their applicability across different communication systems with block-based data transmission schemes.
Overall, the research presents a comprehensive analysis of NN-based equalization approaches, emphasizing their potential to revolutionize digital communication systems through innovative methodologies.
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