Machine Learning-Enhanced DSPM: Towards Intelligent Modulation Techniques

Abstract

The integration of machine learning (ML) into digital signal processing modulation (DSPM) techniques has significantly enhanced the efficiency, adaptability, and accuracy of communication systems. Traditional modulation schemes often face challenges in optimizing signal quality and spectrum efficiency, especially in dynamic and noisy environments. The application of ML-based models in DSPM enables intelligent modulation adaptation, signal classification, and error correction, leading to improved data transmission and network performance. This paper explores the advancements in ML-enhanced DSPM, focusing on deep learning algorithms, reinforcement learning strategies, and real-time adaptive modulation techniques. The study also examines the impact of these intelligent modulation techniques on modern wireless communication networks, including 5G and beyond. The review highlights the benefits of ML-enhanced DSPM, such as increased robustness against interference, lower latency, and enhanced spectral efficiency. Furthermore, challenges related to computational complexity, model training, and real-time implementation are discussed, paving the way for future research directions in intelligent modulation.