Large Scale Fading

Large-scale fading, a significant challenge in wireless communication, refers to the long-term variations in signal strength due to factors like path loss and shadowing. Current research focuses on mitigating its effects through techniques like intelligent reflecting surfaces (IRS) and advanced channel prediction methods employing machine learning models such as convolutional neural networks (CNNs) and deep reinforcement learning (DRL). These efforts aim to improve the reliability and efficiency of wireless systems, particularly in scenarios with high user mobility and dense deployments, impacting areas such as 6G communication and federated learning. The development of robust algorithms for activity detection and data transmission in the presence of large-scale fading is a key objective, with Bayesian approaches and modified AMP networks showing promise.