Summary of Learning Robust Representations For Communications Over Noisy Channels, by Sudharsan Senthil et al.
Learning Robust Representations for Communications over Noisy Channels
by Sudharsan Senthil, Shubham Paul, Nambi Seshadri, R. David Koilpillai
First submitted to arxiv on: 2 Sep 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 introduces FCNNs as an innovative approach to designing end-to-end communication systems, departing from traditional methods and error control coding. By combining information theory and machine learning tools, researchers investigate the use of various cost functions based on mutual information and pairwise distances between codewords to generate robust representations for transmission under strict power constraints. The proposed encoder structure is inspired by the Barlow Twins framework. Experimental results demonstrate that iterative training with randomly chosen noise power levels while minimizing block error rate achieves the best error performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses special kinds of computer networks called FCNNs to create a new way to send messages without using old ideas from classical communications or error correction codes. Scientists combine two important fields – information theory and machine learning – to see how different “cost functions” can help make sure messages get through when there’s not much power left. They also came up with a new way to build the part of the network that takes in data, inspired by something called Barlow Twins. The results show that doing training over and over again while trying to minimize errors works really well. |
Keywords
* Artificial intelligence * Encoder * Machine learning
