A Machine Learning Approach for Bit Error Rate Modeling in Impulsive Noise-Impaired IRS-NOMA Systems

Machine Learning (ML) has been proposed as a powerful tool for designing future cellular networks to meet their requirements. In the realm of 6G communications, IRSNOMA has emerged as an adaptive resource management solution for its performance in resource management. This study explores the use of ML methods to develop prediction models for bit error rate (BER) in IRS-NOMA systems, particularly in impulsive noise environments. The performance of the derived models has been investigated and compared by performing various simulation tests, revealing the effectiveness of ML models in predicting BER for both near and far users in IRS-NOMA systems.