  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.
