Intelligent Reflecting Surface for Spectral Efficiency Maximization in the Multi-User MISO Communication Systems

Abstract

This paper proposes an intelligent reflective surface (IRS) design scheme to improve the spectral efficiency (SE) for downlink multi-user (MU) multiple-input-single-output (MISO) system. IRS composed of low-cost reflecting elements is expected to be used in various scenarios in future wireless communication systems. It can be used even when the direct path is blocked by obstacles. Since the reflecting elements can only adjust the phase, the design of IRS matrix causes a non-convex problem. In this paper, the proposed schemes perform optimization by transforming a non-convex problem into a solvable convex function. Typically, base station-IRS (BS-IRS) channel can be assumed as the line-of-sight (LOS) channel environment. In this case, multi-user system suffers from performance degradation due to the LOS channel rank problem. To alleviate this problem, this paper considers deterministic scattering and sufficient spacing between the reflecting elements of the IRS. The simulation results show that the proposed schemes achieve better SE performance than the randomly generated IRS scheme. In addition, proposed minimum mean square error (MMSE)-based scheme can achieve high performance compared to other schemes even in a low-rank channel environment.INDEX TERMS Intelligent reflecting surface, multi-user MISO, phase shift, spectral efficiency.

Spectral Efficiency Improvement Using Bi-Deep Learning Model for IRS-Assisted MU-MISO Communication System

“…In the case of the DDPG model, SE is less than 4 bps/Hz for 5 dB SNR, and SE is less than 12 bps/Hz for 30 dB SNR. [23], Alternative optimization [44], DL@Based BF [24], and DLPB [45].…”

Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review

“…One user is considered in the services area having a single antenna. The received signal via IRS can be defined as [ 10 ]: where is the received signal at the UE, is the transmitted signal, is the channel matrix from BS to IRS, is the channel vector from IRS to UE, and is the additive white Gaussian noise (AWGN) at the user. diag( ) is a diagonal matrix representing the phase shift values with .…”

“…In recent years, the application of IRS devices has dramatically increased, owing to its excellent convenience in wireless communication, such as mobile edge computing, simultaneous wireless information and power transfer, enhanced physical layer security, device-to-device communication, mmWave massive multiple-input multiple-output (MIMO), unmanned aerial vehicles communication for smart cities, and intelligent internet of things (IoT) applications for wireless sensor networks [ 7 ]. In the state-of-the-art, many studies have been conducted on the design of IRS-aided communication for the different performance matrices [ 8 , 9 , 10 ]. The study conducted in IRS can be categorized as cascade channel estimation, phase shift optimization, beamforming optimization, multiuser communication, and reflecting metasurface grouping [ 11 , 12 , 13 , 14 ].…”

Generalized Superimposed Training Scheme in IRS-Assisted Cell-Free Massive MIMO Systems

“…For beyond 5G and 6G, IRS based applications are investigated in [35]- [37]. Further, with IRS, the works like stochastic geometry analysis for centralized and local processing systems [38], [39], joint-transmit beamforming with reflectors designs [40], spectral-efficiency maximization [41], and more (e.g., energy harvesting, relaying, secrecy rate) are carried out. The required number of reflective elements is investigated in [42].…”