Optimal features-based channel selection and neural network learning for LTE applications

Abstract

Nowadays, the number of internet users is increasing vastly. Hence, predicting the number of channels for user’s communication is a major task. Therefore, scheduling of all traffic flows within the communication services in Long Term Evolution (LTE) scheduling is done by verifying the channel information and user availability in the network. In view of that, this paper proposes a novel feature extraction and classification method to evaluate the user availability status and the channel information for the betterment of communication within the LTE network. For preprocessing, we present a Fast Independent Component Analysis (FICA) method which incorporates dimensionality reduction in the given input feature data during the training course. In this work, feature extraction algorithm is used to extract the network feature and its degree of angle by means of distance between the features set. Subsequently, analyzes the extracted categories of network architecture by the analyzing weight value of that attribute based on the responses. As a result both the feature identification and framing of network structure increases the performance of data mining analysis. Hence, proposing a novel optimization technique like Particle Connected Cuckoo Search (PCCS) optimization algorithm for selecting the best features in the training feature set. Based on the extracted features, the classification method is performed in order to predict the network category by using Multilevel Neural Network (MLNN) classification technique. At this point, a novel kernel model for classification process is incorporated to reduce the time complexity. After that, the information is passed to the LTE scheduling system for providing enhanced communication. The comparative analysis between proposed techniques with the existing methods such as naive Bayes, stacking C, DTNB, random forest, J48, Ridor, decision table, zero R, grading in terms of accuracy, RMSE, CCI, ICI assures that the effectiveness of the proposed MLNN classification method.