# ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection

## TL;DR

ViterbiNet is a novel deep learning-based symbol detection method that integrates neural networks into the Viterbi algorithm, enabling CSI-free, adaptive, and robust performance in dynamic channels.

## Contribution

This work introduces ViterbiNet, a data-driven detector that embeds DNNs into the Viterbi algorithm to operate without channel state information and adapt to changing conditions.

## Key findings

- ViterbiNet approaches the performance of CSI-based Viterbi detection.
- It can track time-varying channels without additional training.
- ViterbiNet is robust to CSI uncertainty and complex channel models.

## Abstract

Symbol detection plays an important role in the implementation of digital receivers. In this work, we propose ViterbiNet, which is a data-driven symbol detector that does not require channel state information (CSI). ViterbiNet is obtained by integrating deep neural networks (DNNs) into the Viterbi algorithm. We identify the specific parts of the Viterbi algorithm that are channel-model-based, and design a DNN to implement only those computations, leaving the rest of the algorithm structure intact. We then propose a meta-learning based approach to train ViterbiNet online based on recent decisions, allowing the receiver to track dynamic channel conditions without requiring new training samples for every coherence block. Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the CSI-based Viterbi algorithm, and is capable of tracking time-varying channels without needing instantaneous CSI or additional training data. Moreover, unlike conventional Viterbi detection, ViterbiNet is robust to CSI uncertainty, and it can be reliably implemented in complex channel models with constrained computational burden. More broadly, our results demonstrate the conceptual benefit of designing communication systems to that integrate DNNs into established algorithms.