Light Weight Deep Learning Algorithm for Voice Call Quality of Services (Qos) in Cellular Communication

Autor: Mritha Ramalingam, S. J. Sultanuddin, N. Nithya, T. F. Michael Raj, T. Rajesh Kumar, S. J. Suji Prasad, Essam A. Al-Ammar, M. H. Siddique, Sridhar Udayakumar
Rok vydání: 2022
Předmět:
Zdroj: Computational Intelligence and Neuroscience. 2022:1-8
ISSN: 1687-5273
1687-5265
DOI: 10.1155/2022/6084044
Popis: In this paper, a deep learning algorithm was proposed to ensure the voice call quality of the cellular communication networks. This proposed model was consecutively monitoring the voice data packets and ensuring the proper message between the transmitter and receiver. The phone sends its unique identification code to the station. The telephone and station maintain a constant radio connection and exchange packets from time to time. The phone can communicate with the station via analog protocol (NMT-450) or digital (DAMPS, GSM). Cellular networks may have base stations of different standards, which allow you to improve network performance and improve its coverage. Cellular networks are different operators connected to each other, as well as a fixed telephone network that allows subscribers of one operator to another to make calls from mobile phones to landlines and from landlines to mobiles. The simulation is conducted in Matlab against different performance metrics, that is, related to the quality of service metric. The results of the simulation show that the proposed method has a higher QoS rate than the existing method over an average of 97.35%.
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje