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Comprehensive Literature Survey for mm-Wave Massive MIMO Using Machine Learning for 6G

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ICCCE 2021

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 828))

Abstract

Sixth Generation (6G) wireless communication networks are highly trained and more capable to overcome the limitations of Fifth Generation (5G) wireless networks. 6G will be expected to fulfil the demands of users from all aspects such as data rate, latency, security, privacy, and so on. Millimetre Wave (mm-wave) massive Multiple inputs multiple outputs (MIMO) will continue to contribute the benefits of higher data rate and better connectivity in 6G wireless systems. Machine learning (ML) is important for mm-wave Massive MIMO, because of its wide range of applications and its incredible ability to adapt and provide solutions to complex problems efficiently, effectively, and quickly. This paper presents the comprehensive literature survey which comprises of how to use the ML/Deep Learning (DL) techniques for the physical layer to optimize different parameters like channel coding and modulation, synchronization, beamforming, positioning, and channel estimation. DL techniques are most suitable for the physical layer to optimized better performance in Bit Error Rate (BER), Symbol Error Rate (SER), and Signal to Noise Ratio (SNR).

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Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, G H Raisoni College of Engineering & Management, Pune, India

    Rohini Devnikar & Vaibhav Hendre

Authors
  1. Rohini Devnikar
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  2. Vaibhav Hendre
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Correspondence to Rohini Devnikar .

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Editors and Affiliations

  1. BioAxis DNA Research Centre (P) Ltd., Hyderabad, India

    Amit Kumar

  2. Dynexsys, Sydney, NSW, Australia

    Stefan Mozar

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Devnikar, R., Hendre, V. (2022). Comprehensive Literature Survey for mm-Wave Massive MIMO Using Machine Learning for 6G. In: Kumar, A., Mozar, S. (eds) ICCCE 2021. Lecture Notes in Electrical Engineering, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-16-7985-8_80

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  • DOI: https://doi.org/10.1007/978-981-16-7985-8_80

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