Mete YILDIRIM, Radosveta İvanova SOKULLU

Frame Detection with Deep Learning

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.

Keywords:

Communication, correlator, deep learning frame detection, neural network,

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Massey, J. 1972. Optimum frame synchronization. IEEE transactions on communications; 20(2): 115–119.
Scholtz, R. 1980. Frame synchronization techniques. IEEE transactions on communications; 28(8): 1204–1213.
Ibnkahla, M. 2000. Applications of neural networks to digital communications–a survey. Signal processing; 80(7): 1185–1215.
Qin Z, Ye H, Li G, Juang F. 1972. Deep learning in physical layer communications. ArXiv: 1807.11713
Simeone, O. 2018. A very brief introduction to machine learning with applications to communication systems. IEEE Transactions on Cognitive Communications and Networking; 4(4): 648–664.
Wang T, Wen CK, Wang H, Gao F, Jiang T. 2017. Deep learning for wireless physical layer: Opportunities and challenges. China Communications; 14(11): 92–111.
Dörner S, Cammerer S, Hoydis J, Brink S. 2017. Deep learning based communication over the air. IEEE Journal of Selected Topics in Signal Processing; 12(1): 132–143.
O’Shea T, Hoydis J. 2017. An introduction to deep learning for the physical layer. IEEE Transactions on Cognitive Communications and Networking; 3(4): 563–575.
O’Shea T J, Karra K, Clancy T C. 2016. Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention. arXiv: 1608.06409.
TO’Shea T J, Roy T, West N, Hilburn B C. 2018. Physical Layer Communications System Design Over-the-Air Using Adversarial Networks. ArXiv: 1803.03145.
O’Shea T J, Hoydis J. 2017. An introduction to machine learning communications systems. ArXiv: 1702.
O’Shea T J, Erpek T, Clancy T.C. 2017. Deep learning based MIMO communications. arXiv:1707.07980.
TO’Shea T J, Roy T, West N, Hilburn B C. 2018. Physical Layer Communications System Design Over-the-Air Using Adversarial Networks. ArXiv: 1803.03145.
O’Shea T J, Hoydis J. 2017. An introduction to machine learning communications systems. ArXiv: 1702.
LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature; 521(7553): 4362
Schmidl T M, Cox D C. 1997. Robust frequency and timing synchronization for OFDM. IEEE transactions on communications; 45(12): 1613–1621.