Wireless Interference Identification in 5G Smart Networks

Abstract

In the rapidly evolving landscape of 5G communication, the identification and mitigation of wireless interference is paramount to maintaining the integrity and efficiency of data transmission. This chapter delves into the intricate process of wireless interference identification, emphasizing its critical role in the predictive analysis of modulation classification and the implementation of adaptive modulators. The discussion begins with a comprehensive overview of 5G architecture, and the inherent challenges posed by dense signal environments. Key techniques for interference identification are explored, including advanced machine learning algorithms and spectrum sensing methods that enable real-time detection and characterization of interference sources. The chapter then examines how these identification techniques inform the predictive analysis of modulation classification. By accurately predicting the modulation scheme of incoming signals, the system can adaptively adjust its modulator settings, thereby optimizing performance and minimizing error rates.