AI-SynPerf: Synthetic Data Intelligence Framework for 5G Mobile Performance Simulation
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I1P118Keywords:
Synthetic Data, 5G Simulation, Network Performance, AI Modeling, Digital Twin, Network Optimization, Machine LearningAbstract
To help with design optimization, predictive maintenance as well as network robustness, the quick growth of 5G networks needs more accurate and scalable performance simulations. AI-SynPerf has come up with the latest way to employ artificial intelligence to create, simulate, and assess mobile performance information. This reduces the need for costly actual world measurements. The platform uses advanced generative models, including as GANs along with reinforcement learning agents, to create realistic datasets of traffic, latency, and throughput that show how different networks perform. AI-generated datasets help simulation engines that mimic changing behaviors across these 5G layers. This lets you make predictions about performance bottlenecks, signal interference & the effects of user mobility. AI-SynPerf speeds up and makes network assessment methods more accurate by combining intelligent data synthesis with adaptive simulation modeling. It also cuts down on the time & effort needed to collect their information. Experimental results show that the framework improves their simulation efficiency by up to 30% and is better at correlating with actual world key performance indicators than traditional statistical modeling. The system's predictive component helps communications companies improve their infrastructure before it gets worse by forecasting when it will break down and advocating improvements to the structure. AI-SynPerf is an important advancement in creating simulation spaces that use AI and data that help telecom firms create, test, and further develop 5G networks with greater effectiveness. This type of technology not only streamlines up the procedure of coming up with imaginative concepts, but it also sets up the environment for advanced approaches to modeling the fact that may be implemented in the coming generations of 6G ecosystems.
Downloads
References
[1] Pandey, Chandrasen, et al. "Resource-efficient synthetic data generation for performance evaluation in mobile edge computing over 5g networks." IEEE Open Journal of the Communications Society 4 (2023): 1866-1878.
[2] Zaidi, Syed Muhammad Asad, et al. "SyntheticNET: A 3GPP compliant simulator for AI enabled 5G and beyond." IEEE Access 8 (2020): 82938-82950.
[3] Patil, Anitha. "Synthetic NET: An AI-Enabled 5G and Beyond 3GPP Compliant Simulator." Journal of Algebraic Statistics 13.3 (2022): 2397-2401.
[4] Alhayani, Bilal, et al. "RETRACTED ARTICLE: 5G standards for the Industry 4.0 enabled communication systems using artificial intelligence: perspective of smart healthcare system." Applied nanoscience 13.3 (2023): 1807-1817.
[5] Huang, Xin-Lin, Xiaomin Ma, and Fei Hu. "Machine learning and intelligent communications." Mobile Networks and Applications 23.1 (2018): 68-70.
[6] Parakala, Adityamallikarjunkumar. "Citizen-Facing Automation: Chatbots and Self-Service in Public Services." International Journal of AI, BigData, Computational and Management Studies 4.4 (2023): 108-118.
[7] 7 Dandachi, Ghina, et al. "An artificial intelligence framework for slice deployment and orchestration in 5G networks." IEEE Transactions on Cognitive Communications and Networking 6.2 (2019): 858-871.
[8] Huang, Jie, et al. "A big data enabled channel model for 5G wireless communication systems." IEEE Transactions on Big Data 6.2 (2018): 211-222.
[9] Datla, Lalith Sriram. “Identity Threat Detection: Techniques for Preventing Credential Abuse in Cloud Systems”. International Journal of Emerging Trends in Computer Science and Information Technology, vol. 2, no. 4, Dec. 2021, pp. 95-104
[10] Sevgican, Salih, et al. "Intelligent network data analytics function in 5G cellular networks using machine learning." Journal of Communications and Networks 22.3 (2020): 269-280.
[11] Guntupalli, Bhavitha. "Data Lake Vs. Data Warehouse: Choosing the Right Architecture." International Journal of Artificial Intelligence, Data Science, and Machine Learning 4.4 (2023): 54-64.
[12] Oliveira, Afonso, and Teresa Vazão. "Generating synthetic datasets for mobile wireless networks with sumo." Proceedings of the 19th ACM international symposium on mobility management and wireless access. 2021.
[13] Nardini, Giovanni, et al. "Exploiting Simu5G for generating datasets for training and testing AI models for 5G/6G network applications." SoftwareX 21 (2023): 101320.
[14] Parakala, Adityamallikarjunkumar, and Jyothirmay Swain. "AI‑Powered Intelligent Automation Emerges." International Journal of Artificial Intelligence, Data Science, and Machine Learning 3.4 (2022): 96-106.
[15] Zhao, Jing, Lei Guo, and Yueqiao Li. "Application of digital twin combined with artificial intelligence and 5G technology in the art design of digital museums." Wireless Communications and Mobile Computing 2022.1 (2022): 8214514.
[16] Xiao, Yong, et al. "Toward self-learning edge intelligence in 6G." IEEE Communications Magazine 58.12 (2021): 34-40.
[17] Guntupalli, Bhavitha, and Surya Vamshi Ch. "My Favorite Design Patterns and When I Actually Use Them." International Journal of Emerging Trends in Computer Science and Information Technology 3.3 (2022): 63-71.
[18] Huang, Yongming, et al. "True-data testbed for 5G/B5G intelligent network." Intelligent and Converged Networks 2.2 (2021): 133-149.
[19] Fu, Yu, et al. "Artificial intelligence to manage network traffic of 5G wireless networks." IEEE network 32.6 (2018): 58-64.
[20] Pusapati, Suryanarayanaraju. Cellular Network KPI Prediction on Simulated 5G-NR V2N Traffic Dataset Using Machine Learning. MS thesis. The University of Regina (Canada), 2023.
[21] Vemula, V. R., & Yarraguntla, T. (2021). Mitigating insider threats through behavioural analytics and cybersecurity policies. Int. Meridian J, 3(3), 1-20.
