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

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 8
• Main Authors: Ramalingam, Mritha, Sultanuddin, S. J., Nithya, N., Michael Raj, T. F., Rajesh Kumar, T., Suji Prasad, S. J., Al-Ammar, Essam A., Siddique, M. H., Udayakumar, Sridhar
• Format: Journal Article
• Language: English
• Published: New York Hindawi 30.08.2022; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Algorithms; Analysis; Cell interactions; Cell phones; Cellular communication; Cellular telephones; Communication; Communication networks; Data mining; Deep learning; Internet access; Language; Machine learning; Performance measurement; Personal computers; Quality management; Quality standards; Radio equipment; Receivers & amplifiers; Service introduction; Telecommunication systems; Telephones; Text messaging; Voice communication; Weight reduction; Wireless telecommunications equipment; India
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/6084044

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%.