Summary of Beam Prediction Based on Large Language Models, by Yucheng Sheng et al.
Beam Prediction based on Large Language Models
by Yucheng Sheng, Kai Huang, Le Liang, Peng Liu, Shi Jin, Geoffrey Ye Li
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper develops a high-performing and robust beam prediction method using large language models (LLMs). The authors formulate the millimeter wave (mmWave) beam prediction problem as a time series forecasting task, leveraging cross-variable attention to aggregate historical observations. A trainable tokenizer transforms these representations into text-based inputs for LLMs. By utilizing the prompt-as-prefix (PaP) technique, the method harnesses the power of LLMs to predict future optimal beams. The results demonstrate that this LLM-based approach outperforms traditional learning-based models in prediction accuracy and robustness, showcasing the potential of LLMs in enhancing wireless communication systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer models called large language models (LLMs) to make really good predictions about how radio signals should be directed. Right now, people use old-fashioned ways to figure this out, but these new models can do a much better job! They take the past and present information about how radio signals have been moving, then use that to predict what will happen in the future. By using these special models, they can make more accurate predictions and even handle unexpected situations. This is super important for people who want to improve wireless communication systems like cell phones or Wi-Fi. |
Keywords
» Artificial intelligence » Attention » Prompt » Time series » Tokenizer
