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 in which 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 orders-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 are the two important design principles of the GBAF codes, which not only reduce the communication overhead, due to fewer interactions between the transmitter and receiver, but also enable flexible coding rates. GBAF codes also have a modular structure that can be implemented using different neural network architectures. In this work, we employ the popular transformer architecture, which outperforms all the prior DNN-based code designs in terms in terms of BLER BLER in the low signal-to-noise ratio regime when the feedback channel is noiseless.

I. Introduction

Reliable communication in the presence of noise has been a long-standing challenge. Numerous coding and modulation techniques have been invented over many decades to push the boundaries of communication; that is, to achieve higher data rates with smaller error probability under given resource constraints (bandwidth, power). 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 Bose-Chaudhuri-Hocquenghem (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 block length regime, there are many scenarios where we do not have practical codes that approach the fundamental theoretical boundaries.