Elevating Spectral Efficiency through Quantum-Inspired Deep Learning in Massive MIMO for 5G Communications

As the demand for higher data rates and seamless connectivity escalates in the era of 5G communications, the integration of advanced technologies becomes imperative to address the associated challenges. This review paper explores the transformative potential of Quantum-Inspired Deep Learning (QIDL) techniques in enhancing spectral efficiency within Massive Multiple Input, Multiple Output (MIMO) systems for 5G networks. Drawing inspiration from principles derived from quantum computing, QIDL introduces a paradigm shift in how deep learning models process information. By incorporating the principles of superposition and entanglement, QIDL models exhibit a unique capability to simultaneously explore diverse possibilities, thereby optimizing the utilization of spectral resources. This article gives a thorough review of Massive MIMO technology and its function in 5G communications as it stands right now. The article continues by explaining how the complicated and ever-changing wireless communication situations may be overcome by utilizing QIDL, which is a novel technique. Through a systematic analysis of existing literature and research endeavors, the paper highlights the potential benefits and limitations of employing QIDL in Massive MIMO for enhancing spectral efficiency.