Large Wireless Model (LWM): A Foundation Model for Wireless Channels
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917979453652992 |
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| author | Alikhani, Sadjad Charan, Gouranga Alkhateeb, Ahmed |
| author_facet | Alikhani, Sadjad Charan, Gouranga Alkhateeb, Ahmed |
| contents | This paper presents Large Wireless Model (LWM) -- the world's first foundation model for wireless channels. Designed as a task-agnostic model, LWM generates universal, rich, contextualized channel embeddings (features) that potentially enhance performance across a wide range of downstream tasks in wireless communication and sensing systems. Towards this objective, LWM, which has a transformer-based architecture, was pre-trained in a self-supervised manner on large-scale wireless channel datasets. Our results show consistent improvements in downstream tasks when using the LWM embeddings compared to raw channel representations, especially in scenarios with high-complexity machine learning tasks and limited training datasets. This LWM's ability to learn from large-scale wireless data opens a promising direction for intelligent systems that can efficiently adapt to diverse tasks with limited data, paving the way for addressing key challenges in wireless communication and sensing systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Wireless Model (LWM): A Foundation Model for Wireless Channels Alikhani, Sadjad Charan, Gouranga Alkhateeb, Ahmed Information Theory Signal Processing This paper presents Large Wireless Model (LWM) -- the world's first foundation model for wireless channels. Designed as a task-agnostic model, LWM generates universal, rich, contextualized channel embeddings (features) that potentially enhance performance across a wide range of downstream tasks in wireless communication and sensing systems. Towards this objective, LWM, which has a transformer-based architecture, was pre-trained in a self-supervised manner on large-scale wireless channel datasets. Our results show consistent improvements in downstream tasks when using the LWM embeddings compared to raw channel representations, especially in scenarios with high-complexity machine learning tasks and limited training datasets. This LWM's ability to learn from large-scale wireless data opens a promising direction for intelligent systems that can efficiently adapt to diverse tasks with limited data, paving the way for addressing key challenges in wireless communication and sensing systems. |
| title | Large Wireless Model (LWM): A Foundation Model for Wireless Channels |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2411.08872 |