Blind Equalizer

Blind equalization aims to recover transmitted signals without prior knowledge of the communication channel, a crucial challenge in various applications like high-speed optical networks and wireless communications. Current research heavily utilizes artificial neural networks, including convolutional neural networks, spiking neural networks, and variational autoencoders, often trained in unsupervised or few-shot learning paradigms to adapt to diverse channel conditions and modulation schemes. These advancements offer improved performance over traditional methods, particularly in scenarios with limited training data or severe channel distortions, leading to more robust and efficient communication systems.