Summary of Online Optimization For Learning to Communicate Over Time-correlated Channels, by Zheshun Wu et al.
Online Optimization for Learning to Communicate over Time-Correlated Channels
by Zheshun Wu, Junfan Li, Zenglin Xu, Sumei Sun, Jie Liu
First submitted to arxiv on: 1 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 Machine learning techniques have revolutionized communication systems by effectively tackling channel uncertainty. However, most recent works assume Independently and Identically Distributed (I.I.D.) channels, which is rarely the case in real-world scenarios. This paper breaks this assumption by studying online optimization problems for learning-based communication systems over time-correlated channels. We focus on optimizing channel decoders for fading channels and selecting optimal codebooks for additive noise channels. To achieve this, we develop two online optimization algorithms based on the optimistic online mirror descent framework. Our approach provides theoretical guarantees through a sub-linear regret bound on the expected error probability of learned systems. Simulation experiments validate that our methods can leverage channel correlation to achieve lower average symbol error rates compared to baseline methods, consistent with theoretical findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Learning-based communication systems are becoming increasingly important due to their ability to handle uncertainty in channels. In this paper, researchers studied how machines can learn to communicate over time-correlated channels. They focused on two specific tasks: optimizing channel decoders and selecting codebooks. To achieve this, they developed new algorithms that use the correlation of channels to improve communication systems. The research showed that these algorithms work better than previous methods in certain situations. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Probability
