This paper presents a joint fine time synchronization and channel estimation scheme based on deep learning (DL) for wireless communication systems. The scheme adopts a specific training sequence structure with both cyclic prefixing and cyclic postfixing. It works excellently without setting a search range and a threshold as required by the conventional method based on the same training sequence structure. Simulation results demonstrate that the proposed DL-based scheme has significant performance gains for most cases as compared with the conventional method. With improved time synchronization, better channel estimation performance is achieved accordingly.
Joint Fine Time Synchronization and Channel Estimation Using Deep Learning for Wireless Communication Systems
01.06.2022
546617 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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