Quantum-Inspired Optimization for Multi-Dimensional Adaptive Modulation in Energy-Constrained IoT Networks

Nurpeissova A. Kang J.W. Mukasheva A. Bissembayev A. Iliev T.
2026 Institute of Electrical and Electronics Engineers Inc.

IEEE Access
2026 #14 22080 - 22116 pp.

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 the 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’s d = 2.47). A real-time feasibility analysis using STM32L4 microcontroller constraints validated the sub-10 ms processing requirement 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 scaling sustainable IoT ecosystems.

adaptive modulation , energy efficiency , energy harvesting , Internet of Things , machine learning , multi-objective optimization , quantum-inspired optimization , resource-constrained devices , spectral efficiency , wireless communications

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School of Information Technology and Engineering, Kazakh-British Technical University, Almaty, 050000, Kazakhstan
Korea National University of Transportation, Chungju-si, 27909, South Korea
Department of Telecommunication, University of Ruse, Angel Kanchev, Ruse, 7017, Bulgaria

School of Information Technology and Engineering
Korea National University of Transportation
Department of Telecommunication

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026