Large Wireless Model (LWM): A Foundation Model for Wireless Channels

Autor:	Alikhani, Sadjad, Charan, Gouranga, Alkhateeb, Ahmed
Rok vydání:	2024
Předmět:	Computer Science - Information Theory Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	This paper presents the 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 classification and regression 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. Comment: The LWM model and relevant scripts are available on the LWM website: https://lwm-wireless.net/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.08872 Zobrazit plný text záznamu View this record from Arxiv