AI-Driven QAM Transceivers: Enhancing Wireless Communication with Machine Learning

Quadrature Amplitude Modulation (QAM) is widely used in modern wireless communication systems to transmit data efficiently. Conventional QAM transceivers rely on specialized hardware to swiftly and accurately modulate, transmit, and demodulate signals. However, hardware-based transceivers are costly and slow to adapt to evolving technologies. In this research, we examine the potential of replacing hardware with AI-based models in QAM transceivers while maintaining performance standards. Using a 16QAM graycode constellation, we simulate a QAM transceiver and generate a dataset to train machine learning models for signal decoding. We assess the model’s accuracy, operational speed, and functionality against traditional hardware receivers in real-time processing. The outcomes of this study support costeffective, adaptable, and intelligent communication systems that optimize spectrum efficiency and enhance the reliability of modern wireless networks.