The field of wireless communication systems is moving towards more efficient and reliable methods of channel estimation and resource allocation. Recent research has focused on developing novel techniques for improving channel estimation, such as using Gold sequences and cyclic shift embedded pilots, which have shown significant improvements in estimation accuracy and reduction in pilot overhead. Furthermore, the integration of artificial intelligence and machine learning techniques has enabled the development of more advanced channel estimation methods, such as those using computer vision and causal learning. Additionally, research has also explored the use of terahertz frequencies for wireless communication, which has the potential to provide high data rates and low latency communications. Noteworthy papers in this area include the proposal of a novel channel estimation approach using fractional power allocation and partially decoded data symbols, and the development of a vision-based channel estimation technique that integrates causal reasoning into urban terahertz communication systems. The use of federated learning for terahertz wireless communication has also been explored, which has the potential to enable ultra-fast distributed learning.