Channel Prediction

Channel prediction aims to forecast future wireless channel states, crucial for optimizing communication systems' performance and reducing overhead. Current research heavily utilizes machine learning, employing various architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs), and transformer-based models, often incorporating techniques like multi-task learning and physics-informed neural networks to improve accuracy and generalizability. These advancements are significant for enhancing the efficiency and reliability of 5G and beyond wireless networks, particularly in scenarios with high mobility and rapidly changing channel conditions. Improved channel prediction directly translates to better resource allocation, reduced latency, and increased spectral efficiency.