Channel Noise

Channel noise, the unwanted interference corrupting signals during transmission or processing, is a pervasive problem across diverse applications, from communication systems to image processing and quantum computing. Current research focuses on developing robust methods for mitigating its effects, including Bayesian estimation techniques for tracking non-stationary noise, novel algorithms like maximum correntropy criterion for improved channel estimation in the presence of phase noise, and adaptive weighting schemes for handling channel-wise variations in image data. Addressing channel noise is crucial for improving the reliability and performance of various technologies, ranging from 5G networks and vehicular visible light communication to quantum machine learning algorithms.