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Pipelined Neural Network Assisted Mobility Speed Estimation Over Doubly-Selective Fading Channels
- Source :
- IEEE Wireless Communications; 2024, Vol. 31 Issue: 3 p163-168, 6p
- Publication Year :
- 2024
-
Abstract
- The speed estimation has been widely used for tracking mobile device locations, providing essential information in location/mobility-aware communications, enhancing received signal quality/robustness, and reducing energy consumption and latency. Deep learning can be used to improve the performance constrained by signal/system model. This work focuses on the issues on machine learning (ML) based speed estimation using primary synchronous signal (PSS), which is embedded in the 5G standards, over general time-variant multipath channels. Aiming to reduce the complexity involved in the ML algorithms for the speed estimation in mobile networks, we propose a pipelined ML algorithm to decompose the original ML model into several smaller ones. The advantages of the proposed convolutional neural network (CNN) based approach have been verified by simulations.
Details
- Language :
- English
- ISSN :
- 15361284 and 15580687
- Volume :
- 31
- Issue :
- 3
- Database :
- Supplemental Index
- Journal :
- IEEE Wireless Communications
- Publication Type :
- Periodical
- Accession number :
- ejs66690212
- Full Text :
- https://doi.org/10.1109/MWC.009.2200297