In this letter, an error is identified in Proposition 3 presented in Björnson et al. (2020) which includes an erroneous expression to compute the optimal number of reflecting elements that minimizes the total power consumption and maximizes the energy efficiency in intelligent reflecting surface (IRS)-supported transmission. The correct version of this expression and a few comments are provided in this letter, and simulation results are also presented to support this claim.

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have recently gained a great deal of attention due to their ability of providing full-space service coverage. In this paper, a novel concept called irregular STAR-RIS is proposed with a small portion of activated transmitting and reflecting elements, where users in opposite sides of the surface are served simultaneously. In this proposed system, a tabu search-based sparse deployment and neighbor extraction-based cross-entropy method for the beamforming optimization of the proposed irregular STAR-RIS are considered. For the sake of fair comparison and to highlight the achievable gains, the weighted sum-rate (WSR) performance of a regular STAR-RIS-aided wireless system and a traditional system without STAR-RIS is also evaluated. The results show that the proposed irregular STAR-RIS with a small portion of active elements is capable of significantly improving the WSR performance compared to the conventional systems without STAR-RIS. It is also shown that the position of the users has an impact on the WSR performance, and that the near users have better performance than the far ones.

This paper is accepted and presented in IEEE ICC 2023.

A document by Taissir Elganimi . Click on the document to view its contents.

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.