Interference Cancellation

Interference cancellation aims to remove unwanted signals from a desired signal, a crucial challenge across diverse fields like wireless communication and signal processing. Current research heavily emphasizes data-driven approaches, employing deep learning architectures such as autoencoders, convolutional neural networks, and graph neural networks, often coupled with traditional methods like successive interference cancellation or matched filtering, to achieve superior performance. These advancements are significantly impacting various applications, improving the efficiency and reliability of wireless systems, enhancing the accuracy of signal processing tasks, and enabling new possibilities in areas like federated learning and joint communication-radar systems.