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<p>The swift advancement of wireless communication technology is facilitating the development of 6G and subsequent networks, anticipated to provide unparalleled performance, dependability, and capacity. In this environment, Massive Multiple-Input Multiple-Output (MIMO) systems have emerged as a pivotal technology capable of transforming the wireless landscape. Fifth Generation (5G) telecommunications networks are commercially accessible. M-MIMO systems facilitate their implementation. Future wireless networks will accommodate a greater number of devices, and MIMO networks lack scalability, necessitating innovative solutions. Cell-free Massive MIMO (CF M-MIMO) technology seems to be the most advantageous alternative. Power allocation and channel estimation pose challenges for CF M-MIMO systems, notwithstanding their appeal. Deep Learning (DL) has addressed numerous scientific difficulties, including those in wireless communications. This work examines contemporary deep learning methodologies for cooperative full-duplex massive MIMO communication systems. The discussion also encompasses the most prevalent deep learning models and the attributes of cell-free networks. Ultimately, future research is addressed. This extensive research offers a detailed examination of machine learning (ML) as it pertains to the cutting-edge advancements in Massive MIMO systems, emphasising their influence on the evolution of 6G and subsequent networks. The review commences with a comprehensive analysis of the core principles and essential ideas of Massive MIMO, emphasising its benefits in spatial multiplexing, interference mitigation, and energy efficiency. It explores the progression from 4G to 5G to 6G and beyond, examining the distinct challenges and prerequisites posed by these next-generation networks and how Massive MIMO might mitigate them. The paper provides a comprehensive examination of the current research and advances in Massive MIMO technology, encompassing advancements in antenna design, signal processing methodologies, and network architectures via the lens of Machine Learning (ML). This paper examines studies on enhancing physical layer attributes such as channel coding, modulation, synchronisation, beamforming, positioning, and channel estimation with Machine Learning (ML) methodologies. Deep learning approaches are optimal for the physical layer to enhance bit error rate, symbol error rate, and signal-to-noise ratio. It also explores potential applications across several domains. The paper examines the regulatory and standardisation initiatives influencing the incorporation of Massive MIMO into 6G networks. Furthermore, it tackles the practical obstacles, deployment possibilities, and scaling issues related to Massive MIMO systems. </p>

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A Comprehensive Review on Massive MIMO Systems for 6G and Beyond Networks Using ML and DL Techniques

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Satyanarayana Murthy Nimmagadda,
  • Shaik Fayaz Ahamed,
  • Vijaya Kumar Padarti

摘要

The swift advancement of wireless communication technology is facilitating the development of 6G and subsequent networks, anticipated to provide unparalleled performance, dependability, and capacity. In this environment, Massive Multiple-Input Multiple-Output (MIMO) systems have emerged as a pivotal technology capable of transforming the wireless landscape. Fifth Generation (5G) telecommunications networks are commercially accessible. M-MIMO systems facilitate their implementation. Future wireless networks will accommodate a greater number of devices, and MIMO networks lack scalability, necessitating innovative solutions. Cell-free Massive MIMO (CF M-MIMO) technology seems to be the most advantageous alternative. Power allocation and channel estimation pose challenges for CF M-MIMO systems, notwithstanding their appeal. Deep Learning (DL) has addressed numerous scientific difficulties, including those in wireless communications. This work examines contemporary deep learning methodologies for cooperative full-duplex massive MIMO communication systems. The discussion also encompasses the most prevalent deep learning models and the attributes of cell-free networks. Ultimately, future research is addressed. This extensive research offers a detailed examination of machine learning (ML) as it pertains to the cutting-edge advancements in Massive MIMO systems, emphasising their influence on the evolution of 6G and subsequent networks. The review commences with a comprehensive analysis of the core principles and essential ideas of Massive MIMO, emphasising its benefits in spatial multiplexing, interference mitigation, and energy efficiency. It explores the progression from 4G to 5G to 6G and beyond, examining the distinct challenges and prerequisites posed by these next-generation networks and how Massive MIMO might mitigate them. The paper provides a comprehensive examination of the current research and advances in Massive MIMO technology, encompassing advancements in antenna design, signal processing methodologies, and network architectures via the lens of Machine Learning (ML). This paper examines studies on enhancing physical layer attributes such as channel coding, modulation, synchronisation, beamforming, positioning, and channel estimation with Machine Learning (ML) methodologies. Deep learning approaches are optimal for the physical layer to enhance bit error rate, symbol error rate, and signal-to-noise ratio. It also explores potential applications across several domains. The paper examines the regulatory and standardisation initiatives influencing the incorporation of Massive MIMO into 6G networks. Furthermore, it tackles the practical obstacles, deployment possibilities, and scaling issues related to Massive MIMO systems.