Abstract
Energy-constrained Internet of Things (IoT) networks require adaptive modulation schemes that simultaneously optimize multiple conflicting objectives, such as energy efficiency, throughput, latency, and reliability, under severe hardware limitations. Traditional approaches either optimize single objectives or fail to effectively explore multi-dimensional solution spaces, thereby limiting their performance in resource-constrained environments. This study introduces a quantum-inspired Multi-Dimensional Adaptive Modulation (QI-MDAM) algorithm that leverages quantum-superposition principles to represent modulation selection as probabilistic amplitude states, enabling simultaneous probability-weighted evaluation of multiple modulation candidates through quantum amplitude representation and, maintaining computational efficiency for IoT devices. QI-MDAM demonstrated exceptional performance through a comprehensive evaluation of 11 state-of-the-art approaches across four representative IoT deployment scenarios: a 12.4% improvement in energy efficiency (0.2119 vs. 0.2418 mJ/bit baseline) and a 78.8% throughput enhancement (0.685 vs. 0.383 Mbps baseline). Statistical validation using Monte Carlo simulation with 100 independent instances confirmed highly significant performance differences (Analysis of Variance (ANOVA):F(11,1188)= 47.23,p= 2.47 × 10−7) with large effect sizes (Cohen’sd= 2.47). A real-time feasibility analysis using STM32L4 microcontroller constraints validated the sub-10 ms processing requirements across all scenarios. The quantum-inspired approach achieves superior Pareto frontier positioning, while optimizing conflicting objectives that classical methods cannot reconcile. These results establish quantum-inspired optimization as a paradigm-shifting approach for resource-constrained wireless communications, enabling previously unattainable performance combinations that are critical for the scaling of sustainable IoT ecosystems.