Summary of Near-field Beam Training For Extremely Large-scale Mimo Based on Deep Learning, by Jiali Nie et al.
Near-field Beam training for Extremely Large-scale MIMO Based on Deep Learning
by Jiali Nie, Yuanhao Cui, Zhaohui Yang, Weijie Yuan, Xiaojun Jing
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a novel deep-learning-based approach for efficient near-field beam training in Extremely Large-scale Array (ELAA) systems. The traditional method suffers from significant beam training overhead due to the need for both angle and distance information. To address this, the authors employ a convolutional neural network (CNN) to learn channel characteristics from historical data, utilizing the negative value of the user average achievable rate as the loss function. This method maximizes multi-user networks’ achievable rate without predefined beam codebooks, requiring only pre-estimated channel state information (CSI) for optimal beamforming. The proposed scheme achieves a more stable beamforming gain and significantly improves performance compared to traditional methods, while also substantially diminishing the near-field beam training overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to make wireless communication systems faster and more efficient. It’s called ELAA, which is short for Extremely Large-scale Array. Right now, making sure signals reach their destinations efficiently is a big challenge. The authors came up with an idea to use special kinds of computers called neural networks to learn how to do this job better. They tested it out and found that it makes wireless communication systems work faster and more reliable. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Loss function » Neural network
