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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 researchers 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 correlationbased 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.

Signal-to-noise ratio is a very important parameter that must be known in order to make many communication systems to work efficiently. The determination of Signal-to-noise ratio may cause extra cost, complexity or can cause a decrease in the efficiency of resource allocation depending on the method used. The machine learning methods offer a practical solution while eliminating these negative aspects with supervised learning and offline training. Deep learning stands out with its success as a type of machine learning. In this study, the estimation of the instantaneous value of the signal to noise ratio in digital symbols was investigated using the deep learning technique.