Advanced Quantum-Inspired Models for Maximizing Spectral Efficiency in 5G and 6G Massive MIMO Systems

Improving system performance via maximizing spectral efficiency is becoming increasingly crucial as 5G and 6G communication networks develop to fulfill the increasing need for faster data rates, less latency, and more dependable connectivity. Fundamental to next-generation wireless networks, Advanced Quantum-Inspired Deep Learning (QIDL) models are investigated here to maximize spectrum efficiency inside Massive MIMO (Multiple Input Multiple Output) systems. By enabling the concurrent investigation of numerous resource allocation possibilities, QIDL provides a fresh strategy for dealing with the intricate and ever-changing character of wireless settings, drawing inspiration from quantum computing concepts like entanglement and superposition. In light of the growing complexity of today’s communication networks, this research examines the current state of Massive MIMO technologies and their shortcomings in detail. The article continues by outlining QIDL method integration as a potential way to enhance spectrum use. Experimental results show that the suggested models outperform state-of-the-art approaches in optimising resource allocation, improving spectral efficiency, and decreasing computing complexity in 5G and 6G situations. Finally, this study lays the groundwork for future wireless network research into quantum-inspired approaches and confirms QIDL as a potent tool for addressing spectrum efficiency issues in Massive MIMO systems.