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Self-Supervised and Invariant Representations for Wireless Localization
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DOI：10.1109/TWC.2023.3348203.png)
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Abstract
En 中文
In this work, we present a wireless localization method that operates on self-supervised and unlabeled channel estimates. Our self-supervising method learns general-purpose channel features robust to fading and system impairments. Learned representations are easily transferable to new environments and ready to use for other wireless downstream tasks. To the best of our knowledge, the proposed method is the first joint-embedding self-supervised approach to forsake the dependency on contrastive channel estimates. Our approach outperforms fully-supervised techniques in small data regimes under fine-tuning and, in some cases, linear evaluation. We assess the performance in centralized and distributed massive multiple-input multiple-output (MIMO) systems for multiple datasets. Moreover, our method works indoors and outdoors without additional assumptions or design changes.
Keywords:
Wireless communication
Location awareness
Transformers
Task analysis
Channel estimation
Global Positioning System
Quality of service
Wireless localization
transformer
self-supervised
deep learning
CSI
massive MIMO
Journal
IF:
10.7
Papers:
1.3W
Citations:
5.3W