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Resource-aware deep learning models for beyond-wave-length positioning accuracy in massive MIMO architecture.
- Source :
-
Computers & Electrical Engineering . May2024, Vol. 116, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Localization models, particularly for indoor applications like autonomous vehicles and smart manufacturing in Industry 4.0, have emerged as a crucial research area because of the growing need for precise positioning. In this context, distributed cell-free MIMO have demonstrated capabilities for a higher fine-grained resolution and accuracy. This paper explores different approaches to address challenges related to positioning and corresponding data transmission and model compression problems in cell-free massive MIMO systems. We present a novel deep-learning powered CSI compression model for reducing the signalization data associated with the localization task, minimizing the fronthaul capacity requirement by at least a factor of 2, without a significant loss of accuracy. In our research, we also focus on the challenges of deploying deep end-to-end neural networks on resource-constrained embedded platforms. Two approaches are proposed: model modification and magnitude-based pruning. Our results show that the pruned end-to-end localization model has a smaller size by 36% at the cost of an increase on the localization error by a factor of 1.5 (precision is reduced from approximately 5 mm to 7.8 mm). [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00457906
- Volume :
- 116
- Database :
- Academic Search Index
- Journal :
- Computers & Electrical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 177565453
- Full Text :
- https://doi.org/10.1016/j.compeleceng.2024.109154