The field of convolutional code decoding is witnessing significant developments, with a focus on improving decoding efficiency and error correction capabilities. Researchers are exploring new approaches to decoding, including linear representations of maximum a posteriori probability decoding and novel erasure decoding algorithms. These innovations have the potential to enhance the performance of digital communication systems, such as digital audio broadcasting. Notably, recent work has also investigated the application of subcode ensemble decoding to polar codes, which could lead to improved error correction capabilities in certain scenarios. Noteworthy papers in this area include:

• Optimal Linear MAP Decoding of Convolutional Codes, which proposes a linear representation of BCJR MAP decoding that achieves the same performance as the BCJR MAP decoding but with reduced decoding delay.
• A new method for erasure decoding of convolutional codes, which introduces a novel decoding algorithm using the generator matrix, applicable to catastrophic convolutional codes.
• Subcode Ensemble Decoding of Polar Codes, which extends subcode ensemble decoding to polar codes, enabling improved error correction capabilities without imposing specific design constraints.