Abstract
Big data comprises a large volume of data (i.e., structured and unstructured) stored on a daily basis. Processing such volume of data is a complex task as well as the challenging one. This big data is applied in the cellular network for traffic prediction. Now, benefiting from the big data in cellular networks, it becomes possible to make the analyses one step further into the application level. In order to improve the traffic prediction accuracy with minimum time, Expected Conditional Maximization Clustering and Ruzicka Regression-based Multilayer Perceptron Deep Neural Learning (ECMCRR-MPDNL) technique is introduced. The ECMCRR-MPDNL technique initially collects a large volume of data over the spatial and temporal aspects of cellular networks. Then the collected data are trained with multiple layers such as one input layer, two hidden layers, and one output layer. The activation function is used at the output layer to predict the network traffic based on the similarity value with higher accuracy. These predictors are evaluated using real network traces. Finally, the error rate is calculated for minimizing the prediction error. Experimental evaluation is carried out using a big dataset with different metrics such as prediction accuracy, false-positive and prediction time. The observed result confirms that the proposed ECMCRR-MPDNL technique improves on an average the 98% of performance of network traffic prediction with higher accuracy and 20 % minimum time as well as the false-positive rate as compared to the state-of-the-art methods.
Highlights
With the increasing trend of mobile operators and internet access, the data traffic has posed great challenges since the load of the network is constantly increased
The several works have been designed in the cellular network traffic prediction, but it has some challenges due to the large volume of temporal and spatial dynamics introduced by different user Internet behaviors
The Multilayer precepted deep neural learning concept collects the data from the big datasets and given to the input layer
Summary
With the increasing trend of mobile operators and internet access, the data traffic has posed great challenges since the load of the network is constantly increased. To improve the cellular network traffic prediction accuracy, an ECMCRR-MPDNL technique is introduced by learning the given data using different layers. The ECMCRR-MPDNL technique is introduced to handle the large volume of cellular data for accurate prediction by deeply learning the higher-level features from the rawdatasets using multiple layers [30]. In the first hidden layer, the clustering process is carried out to divide the network data into dissimilar groups for accurately predicting the traffic patterns in the given location with minimum time. Algorithm 1 Expected Conditional Maximization Clustering and Ruzhika Regression-Based Multilayer Precepted Deep Neural Learning Input: Big dataset, Number of data dst , dst , dst2 , . Deep neural learning technique improves the network traffic prediction accuracy and minimizes the false positive rate
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