Feedback is Good, Active Feedback is Better: Block Attention Active Feedback Codes

TL;DR

This paper introduces a novel active feedback coding scheme using transformer-based GBAF codes, significantly improving block error rate performance in feedback channels, especially at low SNR levels.

Contribution

It extends GBAF codes to active feedback scenarios with interacting transformer architectures, achieving state-of-the-art BLER performance.

Findings

Active feedback GBAF codes outperform passive feedback in BLER.

Transformer-based architectures enable effective transmitter-receiver interaction.

Significant improvements at low SNR regimes.

Abstract

Deep neural network (DNN)-assisted channel coding designs, such as low-complexity neural decoders for existing codes, or end-to-end neural-network-based auto-encoder designs are gaining interest recently due to their improved performance and flexibility; particularly for communication scenarios in which high-performing structured code designs do not exist. Communication in the presence of feedback is one such communication scenario, and practical code design for feedback channels has remained an open challenge in coding theory for many decades. Recently, DNN-based designs have shown impressive results in exploiting feedback. In particular, generalized block attention feedback (GBAF) codes, which utilizes the popular transformer architecture, achieved significant improvement in terms of the block error rate (BLER) performance. However, previous works have focused mainly on passive feedback, where the transmitter observes a noisy version of the signal at the receiver. In this work, we show that GBAF codes can also be used for channels with active feedback. We implement a pair of transformer architectures, at the transmitter and the receiver, which interact with each other sequentially, and achieve a new state-of-the-art BLER performance, especially in the low SNR regime.