IRS-assisted beamspace millimeter-wave massive MIMO with interference-aware beam selection

Elganimi, Taissir Y, Elmajdub, Retaj I, Nauryzbayev, Galymzhan and Rabie, Khaled ORCID: https://orcid.org/0000-0002-9784-3703 (2023) IRS-assisted beamspace millimeter-wave massive MIMO with interference-aware beam selection. In: IEEE 96th Vehicular Technology Conference (VTC2022-Fall), 26 September 2022 - 29 September 2022, London/Beijing.

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Abstract

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 (RF) 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 (ZF) 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.