Unknown Channel

Research on "unknown channels" focuses on developing methods to analyze and utilize communication channels whose characteristics are not fully known or modeled a priori. Current efforts center on data-driven approaches, employing neural networks (including GANs and deep unfolding architectures) and semi-supervised learning techniques to build robust communication and signal processing systems. These advancements are crucial for improving the efficiency and reliability of various applications, such as indoor positioning, physical-layer authentication, and data transmission in dynamic environments, by mitigating the need for extensive channel characterization. The ultimate goal is to create adaptable systems that perform well even with incomplete knowledge of the underlying channel.