Search

A letter is submitted

Communications over millimeter-wave (mmWave) frequencies is a key technology for the fifth generation (5G) cellular networks due to the large bandwidth available at mmWave bands. The short wavelength of mmWave bands enables large antenna arrays to be placed on the transceivers which forms massive multiple-input multiple-output (MIMO). Massive MIMO with conventional fully-digital (FD) beamforming is difficult to be implemented due to high power consumption and hardware cost. One of the most effective solutions to this problem is hybrid beamforming which can be used to balance the beamforming gain, hardware implementation cost, and the power consumption. However, due to the non-convex constraints imposed by phase shifters, finding the global optima for the hybrid beamforming system is very challenging with high computational complexity. To address this issue, deep learning (DL)-based hybrid precoding with geometric mean decomposition (GMD) algorithm for narrowband mmWave massive MIMO system is proposed in this paper, where it can directly estimate the hybrid analog and digital precoders (combiners) from a given optimal FD precoder (combiner). Simulation results demonstrated that the proposed hybrid precoding model can more accurately approximate the FD precoding performance.

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.