Intelligent reflecting surface (IRS)-assisted beamspace millimeter-wave (mmWave) multiuser massive multiple input multiple output (MIMO) with interference-aware (IA) beam selection scheme is proposed in this paper. This proposed scheme is capable of intelligently reconfiguring the radio environment and utilizing the beam selection for the sake of reducing the number of required radio frequency $( R$F) chains without any noticeable performance degradation. To ensure a fair comparison, the achievable sum-rate and energy efficiency (EE) performance metrics of the proposed scheme are evaluated and compared to that of IRS-assisted fully-digital systems with zero-forcing $(Z$F) precoding and the conventional systems without the IRS technology. Simulation results demonstrate that the proposed IRS-assisted beamspace mmWave massive MIMO system with IA beam selection algorithm outperforms the conventional system without IRS. It is also shown that the performance improves when the number of reflecting elements is more than the total number of mobile users. Moreover, the proposed scheme can potentially offer higher EE than the conventional schemes. Therefore, this shows that the proposed system can be considered as an alternative solution for the future generation of wireless systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    IRS-Assisted Beamspace Millimeter-wave Massive MIMO with Interference-Aware Beam Selection




    Publication date :

    2022-09-01


    Size :

    724563 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Data-Driven Beams Selection for Beamspace Channel Estimation in Massive MIMO

    Bychkov, Roman / Osinsky, Alexander / Ivanov, Andrey et al. | IEEE | 2021


    Beamspace MIMO-NOMA for Millimeter-Wave Communications Using Lens Antenna Arrays

    Wang, Bichai / Dai, Linglong / Gao, Xiqi et al. | IEEE | 2017


    IRS-Assisted Millimeter-wave Massive MIMO with Transmit Antenna Selection for IoT Networks

    Elganimi, Taissir Y. / Rabie, Khaled M. / Nauryzbayev, Galymzhan | IEEE | 2023


    Swish-Driven GoogleNet for Intelligent Analog Beam Selection in Terahertz Beamspace MIMO

    Zarini, Hosein / Mili, Mohammad Robat / Rastiy, Mehdi et al. | IEEE | 2022


    Calibrated Beam Training for Millimeter-Wave Massive MIMO Systems

    Luo, Xingyi / Liu, Wendong / Wang, Zhaocheng | IEEE | 2019