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README.md
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@@ -21,8 +21,8 @@ Welcome to **LWM** (Large Wireless Model) — a powerful, pre-trained model spec
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LWM provides a **generalized feature extraction framework** that can be applied across diverse wireless communication tasks. From predicting the strongest mmWave beams to classifying line-of-sight (LoS) and non-line-of-sight (NLoS) channels andn much more complex tasks, this model is built to handle the intricacies of complex wireless environments. **Trained on hundred thousands of wireless channel samples**, LWM has been designed to **generalize across diverse scenarios** — from urban cityscapes to synthetic environments, ensuring robust performance on a wide range of downstream tasks.
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<img src="images/lwm.PNG" alt="Alt text" width="1000"/>
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<figcaption>Figure 1: This figure depicts the offline pre-training and online embedding generation process for LWM. The channel is divided into fixed-size patches, which are linearly embedded and combined with positional encodings before being passed through a Transformer encoder. During self-supervised pre-training, some embeddings are masked, and LWM leverages self-attention to extract deep features, allowing the decoder to reconstruct the masked values. For downstream tasks, the generated LWM embeddings enhance performance. The right block shows the LWM architecture, inspired by the original Transformer introduced in the "Attention is all you need" paper.</figcaption>
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</figure>
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LWM provides a **generalized feature extraction framework** that can be applied across diverse wireless communication tasks. From predicting the strongest mmWave beams to classifying line-of-sight (LoS) and non-line-of-sight (NLoS) channels andn much more complex tasks, this model is built to handle the intricacies of complex wireless environments. **Trained on hundred thousands of wireless channel samples**, LWM has been designed to **generalize across diverse scenarios** — from urban cityscapes to synthetic environments, ensuring robust performance on a wide range of downstream tasks.
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<div style="text-align: center;">
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<figure>
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<img src="images/lwm.PNG" alt="Alt text" width="1000"/>
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<figcaption>Figure 1: This figure depicts the offline pre-training and online embedding generation process for LWM. The channel is divided into fixed-size patches, which are linearly embedded and combined with positional encodings before being passed through a Transformer encoder. During self-supervised pre-training, some embeddings are masked, and LWM leverages self-attention to extract deep features, allowing the decoder to reconstruct the masked values. For downstream tasks, the generated LWM embeddings enhance performance. The right block shows the LWM architecture, inspired by the original Transformer introduced in the "Attention is all you need" paper.</figcaption>
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</figure>
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