Estimation of 5G End-to-End Delay through Deep Learning based on Gaussian Mixture Models

Deep learning is used in various applications due to its advantages over traditional Machine Learning (ML) approaches in tasks encompassing complex pattern learning, automatic feature extraction, scalability, adaptability, and performance in general. This paper proposes an End-to-End (E2E) delay estimation method for 5G networks through Deep Learning (DL) techniques based on Gaussian Mixture Models (GMM). In the first step, the components of a GMM are estimated through the Expectation-Maximization (EM) algorithm and are then used as labeled data in a supervised deep-learning stage. A multi-layer neural network model is trained using the labeled data and assuming different lengths for each training sample. The accuracy and computation time of the proposed Deep Learning Estimator based on the Gaussian Mixture Model (DLEGMM) are evaluated for various inputs in different 5G network scenarios. The simulation results show that the DLEGMM outperforms the GMM method based on EM in terms of the accuracy of the E2E delay estimates. The estimation method is characterized for different 5G scenarios, showing that when compared to GMM, DLEGMM reduces the mean squared error (MSE) obtained with GMM between 1.8 to 2.7 times.