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<p>Massive multiple-input multiple-output (MIMO) and millimetre-wave (mmWave) technologies are integral for satisfying the increased data rate requirements of the next generation of wireless communication systems. Massive MIMO provides a significant increase in spectral efficiency (SE) but large antenna arrays may limit energy efficiency and increase complexity due to having more radio frequency (RF) chains. For these reasons, antenna selection is crucial for maximizing the system with reduced energy consumption. In this study proposes a Spectral Efficiency- Enhanced Emperor Penguin Optimization (SA-EEPO) algorithm for optimal antenna selection in massive MIMO systems. The The SA-EPO algorithm, also referred to as EEPO was inspired by the huddling behavior of the emperor penguin and explores Manhattan distance for faster convergence to optimal in grid-like search spaces as well as dynamically selecting antennas based on selected power consumption, SE and throughput criteria. The EEPO algorithm aims to minimize the number of active antennas while maximizing potential system performance. Simulation tests in MATLAB R2022a confirm that EEPO significantly outperforms traditional optimization methods including EPO, BFO, PSO, and GA. In fact, EEPO achieves a maximum SE of 23.16 bps/Hz at 30 dB SNR and throughput (24.48 bps), outperforming the remaining methods at all SNR levels. Furthermore, EEPO reduced power consumption to 1.93&#xa0;W considering four chosen antennas which is considerably lower than the existing approaches. Overall, the study illustrates the stability and energy-efficient property of EEPO as a viable solution for large-scale MIMO systems.</p>

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Optimized Antenna Selection for Massive Mimo Systems Using Enhanced Emperor Penguin Optimization for Improved Energy and Spectral Efficiency

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Deepika Patil,
  • Suryakanth Baradabadi

摘要

Massive multiple-input multiple-output (MIMO) and millimetre-wave (mmWave) technologies are integral for satisfying the increased data rate requirements of the next generation of wireless communication systems. Massive MIMO provides a significant increase in spectral efficiency (SE) but large antenna arrays may limit energy efficiency and increase complexity due to having more radio frequency (RF) chains. For these reasons, antenna selection is crucial for maximizing the system with reduced energy consumption. In this study proposes a Spectral Efficiency- Enhanced Emperor Penguin Optimization (SA-EEPO) algorithm for optimal antenna selection in massive MIMO systems. The The SA-EPO algorithm, also referred to as EEPO was inspired by the huddling behavior of the emperor penguin and explores Manhattan distance for faster convergence to optimal in grid-like search spaces as well as dynamically selecting antennas based on selected power consumption, SE and throughput criteria. The EEPO algorithm aims to minimize the number of active antennas while maximizing potential system performance. Simulation tests in MATLAB R2022a confirm that EEPO significantly outperforms traditional optimization methods including EPO, BFO, PSO, and GA. In fact, EEPO achieves a maximum SE of 23.16 bps/Hz at 30 dB SNR and throughput (24.48 bps), outperforming the remaining methods at all SNR levels. Furthermore, EEPO reduced power consumption to 1.93 W considering four chosen antennas which is considerably lower than the existing approaches. Overall, the study illustrates the stability and energy-efficient property of EEPO as a viable solution for large-scale MIMO systems.