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A Review of Energy Efficiency and Power Control Schemes in Ultra-Dense Cell-Free Massive MIMO Systems for Sustainable 6G Wireless Communication

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  • Agbotiname Lucky Imoize

    (Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria
    Department of Electrical Engineering and Information Technology, Institute of Digital Communication, Ruhr University, 44801 Bochum, Germany)

  • Hope Ikoghene Obakhena

    (Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, Ambrose Alli University, Ekpoma 310101, Nigeria
    Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Benin, Benin City 300283, Nigeria)

  • Francis Ifeanyi Anyasi

    (Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, Ambrose Alli University, Ekpoma 310101, Nigeria)

  • Samarendra Nath Sur

    (Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok 737136, Sikkim, India)

Abstract

The traditional multiple input multiple output (MIMO) systems cannot provide very high Spectral Efficiency (SE), Energy Efficiency (EE), and link reliability, which are critical to guaranteeing the desired Quality of Experience (QoE) in 5G and beyond 5G wireless networks. To bridge this gap, ultra-dense cell-free massive MIMO (UD CF-mMIMO) systems are exploited to boost cell-edge performance and provide ultra-low latency in emerging wireless communication systems. This paper attempts to provide critical insights on high EE operation and power control schemes for maximizing the performance of UD CF-mMIMO systems. First, the recent advances in UD CF-mMIMO systems and the associated models are elaborated. The power consumption model, power consumption parts, and energy maximization techniques are discussed extensively. Further, the various power control optimization techniques are discussed comprehensively. Key findings from this study indicate an unprecedented growth in high-rate demands, leading to a significant increase in energy consumption. Additionally, substantial gains in EE require efficient utilization of optimal energy maximization techniques, green design, and dense deployment of massive antenna arrays. Overall, this review provides an elaborate discussion of the research gaps and proposes several research directions, critical challenges, and useful recommendations for future works in wireless communication systems.

Suggested Citation

  • Agbotiname Lucky Imoize & Hope Ikoghene Obakhena & Francis Ifeanyi Anyasi & Samarendra Nath Sur, 2022. "A Review of Energy Efficiency and Power Control Schemes in Ultra-Dense Cell-Free Massive MIMO Systems for Sustainable 6G Wireless Communication," Sustainability, MDPI, vol. 14(17), pages 1-38, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:11100-:d:907338
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    1. Qiu, Tianzhi & Wang, Lunche & Lu, Yunbo & Zhang, Ming & Qin, Wenmin & Wang, Shaoqiang & Wang, Lizhe, 2022. "Potential assessment of photovoltaic power generation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    2. Thompson, Andrew W. & Perez, Yannick, 2020. "Vehicle-to-Everything (V2X) energy services, value streams, and regulatory policy implications," Energy Policy, Elsevier, vol. 137(C).
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