mmWave Signal

Millimeter-wave (mmWave) signals, crucial for high-bandwidth 5G and 6G communication, face challenges from high path loss and susceptibility to blockage. Current research focuses on mitigating these issues through machine learning techniques, employing models like convolutional neural networks, recurrent neural networks (including LSTMs and GRUs), vision transformers, and reinforcement learning algorithms to improve beamforming, blockage prediction, and resource allocation. These advancements are significant for enhancing the reliability and efficiency of mmWave networks, impacting areas such as vehicular communication, healthcare monitoring, and fixed wireless access.