TY  - JOUR
T1  - Frame Detection with Deep Learning
AU  - Yıldırım, Mete
AU  - Sokullu, Radosveta İvanova
PY  - 2021
DA  - June
DO  - 10.18466/cbayarfbe.693942
JF  - Celal Bayar University Journal of Science
JO  - CBUJOS
PB  - Manisa Celal Bayar University
WT  - DergiPark
SN  - 1305-130X
SP  - 209
EP  - 213
VL  - 17
IS  - 2
LA  - en
AB  - Deep learning has become a way of solution for the realization of complex computations. As electronic communication starts to use more complex channels, the systems need to handle tough computations.  For this reason, research on the use of deep learning in communication has increased recently. These researches aim to realize many applications used in communication with deep learning. Frame detection is one of the first things a receiver must handle and it may require a lot of hard computations. Deep learning-based frame detection can be an alternative approach. This study aims to build models that perform frame detection with deep learning. The proposed models provide the performance of correlation-based frame receivers commonly used for frame detection. The mean square root error of the prediction deviation is used as an evaluation metric to compare the proposed model to classic systems.
KW  - Communication
KW  - correlator
KW  - deep learning
KW  - frame detection
KW  - neural network
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UR  - https://doi.org/10.18466/cbayarfbe.693942
L1  - https://dergipark.org.tr/en/download/article-file/981527
ER  -