All you need is feedback: Communication with block attention feedback codes

Deep neural network (DNN)-based channel code designs have recently gained interest as an alternative to conventional coding schemes, particularly for channels where existing codes do not provide satisfactory performance. Coding in the presence of feedback is one such problem, for which promising results have recently been obtained by various DNN-based coding architectures. In this paper, we introduce a novel learning-aided feedback code design, dubbed generalized block attention feedback (GBAF) codes, that achieves order-of-magnitude improvements in block error rate (BLER) compared to existing solutions. Sequence-to-sequence encoding and block-by-block processing of the message bits in GBAF codes not only reduce the communication overhead due to reduced number of interactions between the transmitter and receiver, but also enable flexible coding rates. More importantly, GBAF codes provide a modular structure that can be implemented using different neural network architectures. In this work, we employ the transformer architecture, which outperforms all the prior DNN-based code designs in terms the block error rate in the low signal-to-noise ratio regime when the feedback channel is noiseless. Reliable communication in the presence of noise has been a long-standing challenge. E. Ozfatura, Y. Shao and D. Gündüz are with Information Processing and Communications Lab, Department of Electrical and Electronic Engineering, Imperial College London. Information storage and communication are two core technologies that underpin the information age, and the success of both hinges on error correction codes, such as BCH, Reed-Muller, convolution, turbo, low-density parity-check (LDPC), and polar codes. While these codes can approach the fundamental Shannon capacity limit over an additive white Gaussian noise (AWGN) channel in the large blocklength regime, there are many scenarios where we do not have practical codes that approach the fundamental theoretical boundaries. Coding in the presence of feedback is one such challenging, yet practical scenario. The classical feedback channel model was introduced and studied by Shannon [1].