KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Joint Antenna Selection and Power Allocation Method Based on Quantum Energy Valley Optimization Algorithm for Massive MIMO IoT Systems

  • Xiaoyuan Gu (College of Information and Communication Engineering, Harbin Engineering University) ;
  • Hongyuan Gao (College of Information and Communication Engineering, Harbin Engineering University) ;
  • Jingya Ma (College of Information and Communication Engineering, Harbin Engineering University) ;
  • Shibo Zhang (College of Information and Communication Engineering, Harbin Engineering University) ;
  • Jiayi Wang (College of Information and Communication Engineering, Harbin Engineering University)
  • Received : 2024.04.25
  • Accepted : 2024.10.11
  • Published : 2024.10.31
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Abstract

Massive multiple-input multiple-output (MIMO) has emerged as a pivotal technology to address the escalating communication demands of Internet of Things (IoT). To meet the data transmission needs in IoT systems, we propose an antenna selection method of massive MIMO systems and joint power allocation strategy considering IoT user devices grounded in quantum energy valley optimization (QEVO) in this paper. The derivation of a maximum energy efficiency equation has been established to optimize system resources and provide high quality of service meeting the IoT user devices requirements. To tackle the nonlinear, multiconstrained hybrid optimization challenge proposed for massive MIMO resource allocation in IoT systems, we introduce a quantum energy valley optimization algorithm. This algorithm harnesses the strengths of quantum computation and energy valley optimization (EVO) mechanisms. Simulations indicate that our proposed method can efficiently meet real-time user transmission requirements while markedly enhancing system energy efficiency. When compared with existing power allocation strategies and optimization algorithms applied in massive MIMO communication systems, our approach demonstrates superior performance. The proposed method demonstrates the highest performance across various simulation scenarios when applied to both allocation strategies and system energy efficiency. Our proposed method with highest performance can be properly used on massive IoT devices.

Keywords

Acknowledgement

This work was supported by the National Natural Science Foundation of China (62372131), the postdoctoral scientific research developmental fund of Heilongjiang Province (LBH-Q19098) and the Natural Science Foundation of Heilongjiang Province (LH2020F017).

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