Hybrid HMM Decoder For Convolutional Codes By Joint Trellis-Like Structure and Channel Prior
Haoyu Li, Xuan Wang, Tong Liu, Dingyi Fang, Baoying Liu

TL;DR
This paper introduces a hybrid HMM decoder for convolutional codes that leverages channel state information to significantly improve error correction performance in multipath wireless channels.
Contribution
It proposes a novel HMM-based decoding method with GMM observations that outperforms standard Viterbi decoding, especially in multipath environments.
Findings
Achieves 4.7 dB performance gain with hard-decision decoding.
Achieves 2 dB performance gain with soft-decision decoding.
Demonstrates potential extension to turbo codes.
Abstract
The anti-interference capability of wireless links is a physical layer problem for edge computing. Although convolutional codes have inherent error correction potential due to the redundancy introduced in the data, the performance of the convolutional code is drastically degraded due to multipath effects on the channel. In this paper, we propose the use of a Hidden Markov Model (HMM) for the reconstruction of convolutional codes and decoding by the Viterbi algorithm. Furthermore, to implement soft-decision decoding, the observation of HMM is replaced by Gaussian mixture models (GMM). Our method provides superior error correction potential than the standard method because the model parameters contain channel state information (CSI). We evaluated the performance of the method compared to standard Viterbi decoding by numerical simulation. In the multipath channel, the hybrid HMM decoder…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Wireless Communication Techniques · Error Correcting Code Techniques · Cooperative Communication and Network Coding
