Radio Resource Management Strategy for Mobile Networks Based on QoS Sensible Confederation

Abstract

To attain a higher demand for a high quality of service (QoS) and good data rate to support multimedia streaming the extension of wireless networks came to picture with extreme precision levels. This visualization of the wireless networks next-generation is of various types of radio access technologies such as WiMax, Advance LTE, and Wi-Fi. These all extended forms of wireless networks are based on the heterogeneity of the network.  For achieving this object, a QoS with optimal confederation-aware technology i.e., QOC-RRM technique is discussed as an important input. Hence, this projected article gives an idea about an LTE network i.e., Long Term Evolution for future generation radio resource management. Our proposed technique makes use of the QOC-RRM method. In this hybrid RDNN method i.e., Recurrent Deep Neural Network we present differentiate operators based on multiple constraints through priority wise. This QOC-RRM method controls the due source through the sink or in some cases base stations. Furthermore, for the direction-finding queuing criterion the information with the other CWO i.e., Chaotic Weed Optimization, an algorithm is anticipated. In this method, the sink schedules the available information once the data received at the base station on the first come first basis. The implementation of the projected work is done by using the Network Simulator (NS2) version 2.34 and its extension NS3 tool. The performance outcome shows that the proposed work outperforms as compared to the existing work i.e the conventional RRM scheme. The parameter used for the contrast is the least rate of the data required, the radio spectrums utilization, and the utmost amount of dynamic user.