All you need is feedback: Communication with block attention feedback codes
This addresses a specific challenge in communication systems where conventional codes are ineffective, offering a modular and rate-adaptable solution.
The paper tackles the problem of designing channel codes for feedback communication by introducing generalized block attention feedback (GBAF) codes, achieving order-of-magnitude improvements in error probability compared to existing designs.
Deep learning based channel code designs have recently gained interest as an alternative to conventional coding algorithms, particularly for channels for which existing codes do not provide effective solutions. Communication over a feedback channel is one such problem, for which promising results have recently been obtained by employing various deep learning architectures. In this paper, we introduce a novel learning-aided code design for feedback channels, called generalized block attention feedback (GBAF) codes, which i) employs a modular architecture that can be implemented using different neural network architectures; ii) provides order-of-magnitude improvements in the probability of error compared to existing designs; and iii) can transmit at desired code rates.