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.

I. Introduction

The increasing demand for Internet services and applications is driving transformative developments in communication network technologies. The sixth generation of cellular networks (6G) is emerging in response to the growing need for faster data rates and more efficient connectivity [1]. Designed to support a significantly larger number of devices than current 5G networks, 6G is expected to play a pivotal role in advancing the Internet of Things (IoT) and smart cities in the future [1], [2]. This is accomplished through the strategic utilization of spectrum, advanced network technologies, and innovative techniques to ensure reliable communications. One key innovation gaining attention in the realm of 6G communications is the intelligent reflective surface (IRS). This technology consists of numerous passive reflecting elements that can be precisely adjusted to direct electromagnetic waves, improving coverage, boosting data rates, and enhancing energy efficiency [3]. From an implementation standpoint, the IRS is placed between the transmitter and receiver, altering the phase of the incoming signal to optimize it for the receiver's needs.