Polar Code

Polar codes, a class of error-correcting codes with theoretical optimality guarantees, are a focus of ongoing research aiming to improve their performance and applicability in various communication scenarios. Current research emphasizes developing novel code constructions using deep learning techniques, such as neural networks and transformers, to optimize code design for different decoding algorithms and channel conditions, including those with memory or exhibiting insertion, deletion, and substitution errors. These advancements are significant for enhancing data reliability in applications ranging from 5G wireless communication and DNA storage to distributed machine learning and LiDAR-based object detection. The use of data-driven approaches promises more efficient and robust polar codes tailored to specific application needs.