Leveraging AI in 5G Networks for Estimating Signal-to-Interference-and-Noise Ratio
Ziyatbekova G. Jain S. Dasaratha Rao D. Singh M. Tusha Ahluwalia G.
November/December 2025John Wiley and Sons Inc
Internet Technology Letters
2025#8Issue 6
A key role for artificial intelligence (AI) is anticipated in the era of 5G networks. Effective radio resource management has become more and more important for network operators. But when new technologies, network topologies, and sophisticated equipment are integrated more quickly, its getting harder to allocate enough radio resources for precise channel condition assessment in mobile networks. Predicting channel conditions automatically helps to make effective use of resources. Research presents the mutated gray wolf-driven spiking neural network (MGW-SNN) model, an ML-based method for signal-to-interference-and-noise ratio (SINR) estimation based on the cyber-physical system (CPS) location. An MGW-SNN model uses the present position of the CPS to forecast the SINR. Initially, obtain the data to validate suggested algorithms. Min–max normalization and noise reduction were used to preprocess after collecting data. The spiking neural network (SNN) structures parameters are optimized with the usage of the gray wolf optimization (GWO) method to improve the networks performance. The research was implemented using MATLAB. The proposed strategy in terms of accuracy (93%), RMSE (1.35), (Formula presented.) (0.99), MAE (0.75), and average SINR (10 dB) is validated. It shows that the proposal is effective in predicting the SINR. The approach stabilizes the computational economy while simultaneously increasing accuracy, which makes it suitable for real-time applications in dynamic network environments.
channel condition prediction , cyber-physical system (CPS) , gray wolf optimization (GWO) , radio resource management , signal-to-interference-and-noise ratio (SINR) , slice protection , spiking neural network (SNN)
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Department of Information Systems, Al-Farabi Kazakh National University, Almaty, Kazakhstan
Electronics Engineering Department, Medi-Caps University, Indore, India
Independent Researcher, R&D, United States
Centre of Research Impact and Outcome, Chitkara University, Punjab, Rajpura, India
Quantum University Research Center, Quantum University, Uttarakhand, Roorkee, India
Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh, India
Department of Information Systems
Electronics Engineering Department
Independent Researcher
Centre of Research Impact and Outcome
Quantum University Research Center
Chitkara Centre for Research and Development
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