Summary of Gnn-based Auto-encoder For Short Linear Block Codes: a Drl Approach, by Kou Tian et al.
GNN-based Auto-Encoder for Short Linear Block Codes: A DRL Approach
by Kou Tian, Chentao Yue, Changyang She, Yonghui Li, Branka Vucetic
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel auto-encoder-based end-to-end channel encoding and decoding framework that integrates deep reinforcement learning (DRL) and graph neural networks (GNN). The proposed approach optimizes key coding performance metrics such as error-rates and code algebraic properties by modeling the generation of code parity-check matrices as a Markov Decision Process (MDP). The framework includes an edge-weighted GNN (EW-GNN) decoder that operates on the Tanner graph with an iterative message-passing structure. Once trained on a single linear block code, the EW-GNN decoder can be directly used to decode other linear block codes of different code lengths and code rates. The paper demonstrates the effectiveness of the proposed approach through simulation results, showing significant performance gains over traditional coding schemes such as LDPC with belief propagation (BP) decoding, maximum-likelihood decoding (MLD), and BCH with BP decoding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to encode and decode information using artificial intelligence. It combines two techniques: deep reinforcement learning and graph neural networks. This helps improve the quality of the encoded information by making it more error-resistant. The system is trained on one type of code, but can then be used to decode other types of codes with different lengths and rates. The results show that this approach outperforms traditional methods in certain situations. |
Keywords
» Artificial intelligence » Decoder » Encoder » Gnn » Likelihood » Reinforcement learning
