A Hybrid Artificial Intelligence Model for Predicting Quality of Service (QoS) in 5G Networks Based on Wireless Channel Parameters
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Abstract
This paper proposes a hybrid artificial intelligence (AI) model that combines an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to predict the Quality of Service (QoS) in 5G networks. The model utilizes radio channel indicators such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI), and Channel Quality Indicator (CQI) to forecast throughput and latency levels. These indicators are critical factors affecting network performance; however, the nonlinear relationship among them makes traditional analytical models inadequate for accurate QoS prediction. The importance of the current study lies in estimating QoS in 5G networks by combining radio-level physical-layer indicators into a neural network based on the PSO algorithm. The proposed approach allows for better modeling of QoS dynamics and improvement in prediction accuracy. The hybrid ANN-PSO model that integrates an Artificial Neural Network (ANN) with a Particle Swarm Optimization (PSO) algorithm is compared with traditional methods such as Linear Regression and Random Forest to evaluate prediction accuracy. Experimental results demonstrate that the proposed model achieves higher accuracy and lower prediction error, making it a promising tool for predictive QoS optimization in next-generation 5G systems.
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Ahmadi, S. 2019. 5G NR: architecture, technology, implementation, and operation of 3GPP new radio standards. London: Academic Press.
Barmpounakis, S., Magoula, L., Koursioumpas, N., Khalili, R., Perdomo, J.M. and Manjunath, R.P. 2021. LSTM-based QoS prediction for 5G-enabled connected and automated mobility applications. In: Proceedings of the 2021 IEEE 4th 5G World Forum (5GWF). Piscataway, NJ: IEEE, pp. 436–440. https://doi.org/10.1109/5GWF52925.2021.00083.
Chen, M., Challita, U., Saad, W., Yin, C. and Debbah, M. 2019. Artificial neural networks-based machine learning for wireless networks: a tutorial. IEEE Communications Surveys & Tutorials, 21(4), pp. 3039–3071. https://doi.org/10.1109/COMST.2019.2926625.
Fakhouri, H.N., Alawadi, S., Awaysheh, F.M., Hani, I.B., Alkhalaileh, M. and Hamad, F., 2023. A comprehensive study on the role of machine learning in 5G security: Challenges, technologies, and solutions. Electronics, 12(22), P. 4604. https://doi.org/10.3390/electronics12224604.
Farhoudi, M., Shokrnezhad, M. and Taleb, T. 2023. QoS-aware service prediction and orchestration in cloud-network integrated beyond 5G. In: GLOBECOM 2023 – 2023 IEEE Global Communications Conference. Piscataway, NJ: IEEE, pp. 369–374. https://doi.org/10.1109/GLOBECOM54140.2023.10436905.
Fu, Y., Wang, S., Wang, C.X., Hong, X. and McLaughlin, S. 2018. Artificial intelligence to manage network traffic of 5G wireless networks. IEEE Access, 6, pp. 58–64.
Haidine, A., Salmam, F.Z., Aqqal, A. and Djannati-Ataï, A. 2021. Artificial intelligence and machine learning in 5G and beyond: A survey and perspectives. In: IntechOpen. https://doi.org/10.5772/intechopen.98517.
Houssein, E.H., Gad, A.G., Hussain, K. and Suganthan, P.N. 2021. Major advances in particle swarm optimization: theory, analysis, and application. Swarm and Evolutionary Computation, 63, P. 100868. https://doi.org/10.1016/j.swevo.2021.100868.
Islam, M.A., Siddique, H., Zhang, W. and Haque, I. 2023. A deep neural network-based communication failure prediction scheme in 5G RAN. IEEE Transactions on Network and Service Management, 20(2), pp. 1140–1152. https://doi.org/10.1109/TNSM.2022.3229658.
Iyer, A.P., Li, L.E. and Stoica, I. 2017. Automating diagnosis of cellular radio access network problems. In: Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking. New York, NY: ACM, pp. 79–87.
Jiang, H., Li, Q., Jiang, Y., Shen, G., Sinnott, R., Tian, C. and Xu, M. 2021. When machine learning meets congestion control: A survey and comparison. Computer Networks, 192, P. 108033. https://doi.org/10.1016/j.comnet.2021.108033.
Jonsson, P., 2022. Ericsson mobility report (Review. Part II). Synchroinfo Journal, 8(4), pp. 33-41.
Kavehmadavani, F., Nguyen, V.D., Vu, T.X. and Chatzinotas, S., 2023. Intelligent traffic steering in beyond 5G open RAN based on LSTM traffic prediction. IEEE Transactions on Wireless Communications, 22(11), pp. 7727-7742. http://arxiv.org/abs/2210.08829.
Kennedy, J. and Eberhart, R.C. 1995. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. Piscataway, NJ: IEEE, pp. 1942–1948. https://doi.org/10.1109/ICNN.1995.488968.
Ketkar, N. and Moolayil, J. 2021. Deep learning with Python: learn best practices of deep learning models with PyTorch. Berkeley, CA: Apress. https://doi.org/10.1007/978-1-4842-5364-9.
Lawson, M., Naemi, R., Needham, R.A. and Chockalingam, N. 2024. Can machine learning predict running kinematics based on upper trunk GPS-based IMU acceleration? A novel method of conducting biomechanical analysis in the field using artificial neural networks. Applied Sciences, 14(5). https://doi.org/10.3390/app14051730.
Lee, J., Lee, H. and Park, J. 2017. Fair rate control in 5G cellular networks: user equipment-agnostic approach. In: IEEE Global Communications Conference (GLOBECOM). Piscataway, NJ: IEEE, pp. 1–7. https://doi.org/10.1109/GLOCOM.2017.8254594.
Liu, H., Wang, Y. and Hu, B. 2019. Application research of artificial intelligence in the fifth-generation mobile communication technology. In: IEEE International Conference on Software Engineering and Service Sciences.
Piscataway, NJ: IEEE, pp. 498–501. https://dl.acm.org/doi/pdf/10.1145/2699343.2699345
Lu, F., Du, H., Jain, A., Voelker, G.M., Snoeren, A.C. and Terzis, A. 2015. CQIC: revisiting cross-layer congestion control for cellular networks. In: Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications. New York, NY: ACM, pp. 45–50. https://doi.org/10.1109/GLOCOM.2017.8254594.
Miceli, P.A., Blair, W.D. and Brown, M.M. 2018. Isolating random and bias covariances in tracks. In: 2018 21st International Conference on Information Fusion (FUSION). Piscataway, NJ: IEEE. https://doi.org/10.23919/ICIF.2018.8455530.
Mohammadi, M., Al-Fuqaha, A., Sorour, S. and Guizani, M. 2018. Deep learning for IoT big data and streaming analytics: a survey. IEEE Communications Surveys & Tutorials, 20(4), pp. 2923–2960. https://doi.org/10.1109/COMST.2018.2844341.
Najm, I.A., Hamoud, A.K., Lloret, J. and Bosch, I. 2019. Machine learning prediction approach to enhance congestion control in 5G IoT environment. Electronics, 8(6). https://doi.org/10.3390/electronics8060607.
Nasimi, M., Habibi, M.A., Han, B. and Schotten, H.D. 2021. Edge-assisted congestion control mechanism for 5G network using software-defined networking. In: International Symposium on Wireless Communication Systems. Piscataway, NJ: IEEE, pp. 1–5. https://doi.org/10.1109/ISWCS.2018.8491233.
Qamar, F., Hindia, M.H.D.N., Abbas, T., Dimyati, K.B. and Amiri, I.S. 2019. Investigation of QoS performance evaluation over 5G network for indoor environment at millimeter wave bands. https://doi.org/10.24425/ijet.2019.126288.
Qu, K. 2024. Research on linear regression algorithm. MATEC Web of Conferences, 395, P. 01046. https://doi.org/10.1051/matecconf/202439501046.
Salman, H.A., Kalakech, A. and Steiti, A. 2024. Random forest algorithm overview. Babylonian Journal of Machine Learning, pp. 69–79. https://doi.org/10.58496/BJML/2024/007.
Trinh, H.D., Giupponi, L. and Dini, P. 2018. Mobile traffic prediction from raw data using LSTM networks. In: IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). Piscataway, NJ: IEEE, pp. 1827–1832. https://doi.org/10.1109/PIMRC.2018.8581000.
Vandeput, N. 2021. Data science for supply chain forecasting. Berlin: De Gruyter.
Waleed, S., Ullah, I., Khan, W.U., Rehman, A.U., Rahman, T. and Li, S. 2021. Resource allocation of 5G network by exploiting particle swarm optimization. Iran Journal of Computer Science, 4(3), pp. 211–219. https://doi.org/10.1007/s42044-021-00091-5.
Wang, C.X., Di Renzo, M., Stanczak, S., Wang, S. and Larsson, E.G., 2020. Artificial intelligence enabled wireless networking for 5G and beyond: Recent advances and future challenges. IEEE Wireless Communications, 27(1), pp.16-23.
Wolosz, K., Bodin, U. and Riliskis, L. 2012. A measurement study for predicting throughput from LQI and RSSI. In: Lecture Notes in Computer Science. Berlin: Springer, pp. 89–92. https://doi.org/10.1007/978-3-642-34976-8_10.
Yurshin, V.G. and Stanovov, V.V. 2023. Artificial neural network architecture tuning algorithm. In: EPCT HMMOCS 2022 International Workshop on Hybrid Methods of Modeling and Optimization in Complex Systems. https://doi.org/10.15405/epct.23021.29.
Zhang, C., Patras, P. and Haddadi, H. 2019. Deep learning in mobile and wireless networking: a survey. IEEE Communications Surveys & Tutorials, 21(3). https://doi.org/10.1109/COMST.2019.2904897.
Zhang, C., Ueng, Y.L., Studer, C. and Burg, A. 2020. Artificial intelligence for 5G and beyond 5G: implementations, algorithms, and optimizations. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 10(2), pp. 149–153.
Zhang, H., Wang, D., Zhang, W., Tan, L., Kibalya, G., Zhang, P. and Kuznetsov, K.I. 2023. QoS prediction in intelligent edge computing based on feature learning. Journal of Cloud Computing, 12(1). https://doi.org/10.1186/s13677-023-00391-0.
Zhong, Z., Hamchaoui, I., Khatoun, R. and Serhrouchni, A. 2018. Performance evaluation of CQIC and TCP BBR in mobile network. In: International Conference on Information Networking (ICIN). Piscataway, NJ: IEEE, pp. 1–5.