Light weight deep learning algorithm for voice call quality of services (Qos) in cellular communication
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 iden...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi Publishing Corporation
2022
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/45442/ |
| _version_ | 1848827419605073920 |
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| author | Ramalingam, Mritha Sultanuddin, S. J. Nithya, N. Raj, T. F. Michael Kumar, T. Rajesh Prasad, S. J. Suji Al-Ammar, Essam A. Siddique, M. H. Udayakumar, Sridhar |
| author_facet | Ramalingam, Mritha Sultanuddin, S. J. Nithya, N. Raj, T. F. Michael Kumar, T. Rajesh Prasad, S. J. Suji Al-Ammar, Essam A. Siddique, M. H. Udayakumar, Sridhar |
| author_sort | Ramalingam, Mritha |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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%. |
| first_indexed | 2025-11-15T04:00:25Z |
| format | Article |
| id | ump-45442 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T04:00:25Z |
| publishDate | 2022 |
| publisher | Hindawi Publishing Corporation |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-454422025-08-20T06:18:20Z https://umpir.ump.edu.my/id/eprint/45442/ Light weight deep learning algorithm for voice call quality of services (Qos) in cellular communication Ramalingam, Mritha Sultanuddin, S. J. Nithya, N. Raj, T. F. Michael Kumar, T. Rajesh Prasad, S. J. Suji Al-Ammar, Essam A. Siddique, M. H. Udayakumar, Sridhar QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering 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%. Hindawi Publishing Corporation 2022-08 Article PeerReviewed pdf en cc_by_4 https://umpir.ump.edu.my/id/eprint/45442/1/Light%20weight%20deep%20learning%20algorithm%20for%20voice%20call.pdf Ramalingam, Mritha and Sultanuddin, S. J. and Nithya, N. and Raj, T. F. Michael and Kumar, T. Rajesh and Prasad, S. J. Suji and Al-Ammar, Essam A. and Siddique, M. H. and Udayakumar, Sridhar (2022) Light weight deep learning algorithm for voice call quality of services (Qos) in cellular communication. Computational Intelligence and Neuroscience, 2022 (6084044). pp. 1-8. ISSN 1687-5265 (print); 1687-5273 (online). (Published) https://doi.org/10.1155/2022/6084044 https://doi.org/10.1155/2022/6084044 https://doi.org/10.1155/2022/6084044 |
| spellingShingle | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering Ramalingam, Mritha Sultanuddin, S. J. Nithya, N. Raj, T. F. Michael Kumar, T. Rajesh Prasad, S. J. Suji Al-Ammar, Essam A. Siddique, M. H. Udayakumar, Sridhar Light weight deep learning algorithm for voice call quality of services (Qos) in cellular communication |
| title | Light weight deep learning algorithm for voice call quality of services (Qos) in cellular communication |
| title_full | Light weight deep learning algorithm for voice call quality of services (Qos) in cellular communication |
| title_fullStr | Light weight deep learning algorithm for voice call quality of services (Qos) in cellular communication |
| title_full_unstemmed | Light weight deep learning algorithm for voice call quality of services (Qos) in cellular communication |
| title_short | Light weight deep learning algorithm for voice call quality of services (Qos) in cellular communication |
| title_sort | light weight deep learning algorithm for voice call quality of services (qos) in cellular communication |
| topic | QA75 Electronic computers. Computer science TK Electrical engineering. Electronics Nuclear engineering |
| url | https://umpir.ump.edu.my/id/eprint/45442/ https://umpir.ump.edu.my/id/eprint/45442/ https://umpir.ump.edu.my/id/eprint/45442/ |