Аңдатпа
ABSTRACT 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, it's 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) structure's parameters are optimized with the usage of the gray wolf optimization (GWO) method to improve the network's performance. The research was implemented using MATLAB. The proposed strategy in terms of accuracy (93%), RMSE (1.35), (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.