Neural Network Based Equalizer

Neural network-based equalizers are emerging as powerful alternatives to traditional signal processing methods for mitigating channel impairments in various communication systems, aiming to improve data throughput and reliability. Research currently focuses on optimizing different neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models, often implemented on field-programmable gate arrays (FPGAs) for high-speed applications. These advancements are significant because they offer the potential for substantial performance gains in areas like optical and wireless communications, enabling higher data rates and improved signal quality in challenging environments. Furthermore, techniques like knowledge distillation and unsupervised learning are being explored to enhance efficiency and reduce computational complexity.