Transmit antenna selection for achieving energy efficiency in massive MIMO based 5G cellular network

Authors

  • Janmoni Borah Madanapalle Institute of Technology & Science, Madanapalle, India http://orcid.org/0000-0002-0051-0313
  • Smriti Baruah Madanapalle Institute of Technology & Science, India http://orcid.org/0000-0003-0153-1084
  • Ganjiguntla Bhargavi Madanapalle Institute of Technology & Science, India
  • Palagiri Durgaprasad Madanapalle Institute of Technology & Science, India
  • Boya Damodar Madanapalle Institute of Technology & Science, Andhra Pradesh, India

DOI:

https://doi.org/10.3103/S0735272723020048

Keywords:

Massive MIMO, RUS, MCG, Base Station, Energy efficiency, Antenna

Abstract

In this study, two dynamic algorithms are verified for transmit antenna selection for achieving energy efficiency in Massive MIMO namely: Random User Selection (RUS) and Maximum Channel Gain (MCG). Massive MIMO setups are investigated over N-radio cables via centralized Massive Base Station (BS), which uses RUS and MCG to provide management to U-clients. Massive MIMO networks are shown for 8x8, 16x16, 25x25, 50x50 setups using RUS and MCG. The proficiency of the proposed scheme on setup network has been investigated with RUS and MCG, and then effect on performance metrices such as sumrate, user throughput and energy efficiency have been observed. Power efficiency has also been improved by increasing the number of Massive MIMO antennas. Compared to RUS, MCG provides better service to users located far from the base station antenna.

Author Biographies

Janmoni Borah, Madanapalle Institute of Technology & Science, Madanapalle

ECE, Associate Professor

Smriti Baruah, Madanapalle Institute of Technology & Science

ECE, Assistant Professor

Ganjiguntla Bhargavi, Madanapalle Institute of Technology & Science

ECE, B.Tech Student

Palagiri Durgaprasad, Madanapalle Institute of Technology & Science

ECE, B.tech Student

Boya Damodar, Madanapalle Institute of Technology & Science, Andhra Pradesh

ECE, B.tech Student

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Published

2024-01-16

Issue

Section

Research Articles