Performance Analysis of Reconfigurable Intelligent Surface-Assisted Millimeter Wave Massive MIMO System Under 3GPP 5G Channels
Reconfigurable intelligent surfaces (RIS) and massive multiple input and multiple output (M-MIMO) are the two major enabling technologies for next-generation networks, capable of providing spectral efficiency (SE), energy efficiency (EE), array gain, spatial multiplexing, and reliability. This work...
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MDPI AG
2025-05-01
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| author | Vishnu Vardhan Gudla Vinoth Babu Kumaravelu Agbotiname Lucky Imoize Francisco R. Castillo Soria Anjana Babu Sujatha Helen Sheeba John Kennedy Hindavi Kishor Jadhav Arthi Murugadass Samarendra Nath Sur |
| author_facet | Vishnu Vardhan Gudla Vinoth Babu Kumaravelu Agbotiname Lucky Imoize Francisco R. Castillo Soria Anjana Babu Sujatha Helen Sheeba John Kennedy Hindavi Kishor Jadhav Arthi Murugadass Samarendra Nath Sur |
| author_sort | Vishnu Vardhan Gudla |
| collection | DOAJ |
| description | Reconfigurable intelligent surfaces (RIS) and massive multiple input and multiple output (M-MIMO) are the two major enabling technologies for next-generation networks, capable of providing spectral efficiency (SE), energy efficiency (EE), array gain, spatial multiplexing, and reliability. This work introduces an RIS-assisted millimeter wave (mmWave) M-MIMO system to harvest the advantages of RIS and mmWave M-MIMO systems that are required for beyond fifth-generation (B5G) systems. The performance of the proposed system is evaluated under 3GPP TR 38.901 V16.1.0 5G channel models. Specifically, we considered indoor hotspot (InH)—indoor office and urban microcellular (UMi)—street canyon channel environments for 28 GHz and 73 GHz mmWave frequencies. Using the SimRIS channel simulator, the channel matrices were generated for the required number of realizations. Monte Carlo simulations were executed extensively to evaluate the proposed system’s average bit error rate (ABER) and sum rate performances, and it was observed that increasing the number of transmit antennas from 4 to 64 resulted in a better performance gain of ∼10 dB for both InH—indoor office and UMi—street canyon channel environments. The improvement of the number of RIS elements from 64 to 1024 resulted in ∼7 dB performance gain. It was also observed that ABER performance at 28 GHz was better compared to 73 GHz by at least ∼5 dB for the considered channels. The impact of finite resolution RIS on the considered 5G channel models was also evaluated. ABER performance degraded for 2-bit finite resolution RIS compared to ideal infinite resolution RIS by ∼6 dB. |
| format | Article |
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| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-dfd5ec61ab3d474c9b2d4ba2eb63b32d2025-08-20T01:56:28ZengMDPI AGInformation2078-24892025-05-0116539610.3390/info16050396Performance Analysis of Reconfigurable Intelligent Surface-Assisted Millimeter Wave Massive MIMO System Under 3GPP 5G ChannelsVishnu Vardhan Gudla0Vinoth Babu Kumaravelu1Agbotiname Lucky Imoize2Francisco R. Castillo Soria3Anjana Babu Sujatha4Helen Sheeba John Kennedy5Hindavi Kishor Jadhav6Arthi Murugadass7Samarendra Nath Sur8Communications, Media and Technology Unit, L & T Technology Services, Bangalore 560092, IndiaDepartment of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, IndiaDepartment of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Lagos 100213, NigeriaTelecommunications Department, Universidad Autónoma de San Luis Potosí, San Luis Potosí 78295, MexicoDepartment of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, IndiaDepartment of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, IndiaIndependent Researcher, Satara 415004, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, IndiaDepartment of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok 737102, IndiaReconfigurable intelligent surfaces (RIS) and massive multiple input and multiple output (M-MIMO) are the two major enabling technologies for next-generation networks, capable of providing spectral efficiency (SE), energy efficiency (EE), array gain, spatial multiplexing, and reliability. This work introduces an RIS-assisted millimeter wave (mmWave) M-MIMO system to harvest the advantages of RIS and mmWave M-MIMO systems that are required for beyond fifth-generation (B5G) systems. The performance of the proposed system is evaluated under 3GPP TR 38.901 V16.1.0 5G channel models. Specifically, we considered indoor hotspot (InH)—indoor office and urban microcellular (UMi)—street canyon channel environments for 28 GHz and 73 GHz mmWave frequencies. Using the SimRIS channel simulator, the channel matrices were generated for the required number of realizations. Monte Carlo simulations were executed extensively to evaluate the proposed system’s average bit error rate (ABER) and sum rate performances, and it was observed that increasing the number of transmit antennas from 4 to 64 resulted in a better performance gain of ∼10 dB for both InH—indoor office and UMi—street canyon channel environments. The improvement of the number of RIS elements from 64 to 1024 resulted in ∼7 dB performance gain. It was also observed that ABER performance at 28 GHz was better compared to 73 GHz by at least ∼5 dB for the considered channels. The impact of finite resolution RIS on the considered 5G channel models was also evaluated. ABER performance degraded for 2-bit finite resolution RIS compared to ideal infinite resolution RIS by ∼6 dB.https://www.mdpi.com/2078-2489/16/5/3963GPP 5G channel modellingaverage bit error rate (ABER)massive multiple input and multiple output (M-MIMO)millimeter wave (mmWave)reconfigurable intelligent surfaces (RIS)sixth-generation (6G) |
| spellingShingle | Vishnu Vardhan Gudla Vinoth Babu Kumaravelu Agbotiname Lucky Imoize Francisco R. Castillo Soria Anjana Babu Sujatha Helen Sheeba John Kennedy Hindavi Kishor Jadhav Arthi Murugadass Samarendra Nath Sur Performance Analysis of Reconfigurable Intelligent Surface-Assisted Millimeter Wave Massive MIMO System Under 3GPP 5G Channels Information 3GPP 5G channel modelling average bit error rate (ABER) massive multiple input and multiple output (M-MIMO) millimeter wave (mmWave) reconfigurable intelligent surfaces (RIS) sixth-generation (6G) |
| title | Performance Analysis of Reconfigurable Intelligent Surface-Assisted Millimeter Wave Massive MIMO System Under 3GPP 5G Channels |
| title_full | Performance Analysis of Reconfigurable Intelligent Surface-Assisted Millimeter Wave Massive MIMO System Under 3GPP 5G Channels |
| title_fullStr | Performance Analysis of Reconfigurable Intelligent Surface-Assisted Millimeter Wave Massive MIMO System Under 3GPP 5G Channels |
| title_full_unstemmed | Performance Analysis of Reconfigurable Intelligent Surface-Assisted Millimeter Wave Massive MIMO System Under 3GPP 5G Channels |
| title_short | Performance Analysis of Reconfigurable Intelligent Surface-Assisted Millimeter Wave Massive MIMO System Under 3GPP 5G Channels |
| title_sort | performance analysis of reconfigurable intelligent surface assisted millimeter wave massive mimo system under 3gpp 5g channels |
| topic | 3GPP 5G channel modelling average bit error rate (ABER) massive multiple input and multiple output (M-MIMO) millimeter wave (mmWave) reconfigurable intelligent surfaces (RIS) sixth-generation (6G) |
| url | https://www.mdpi.com/2078-2489/16/5/396 |
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