Reconfigurable Intelligent Surface-Aided MISO Systems with Statistical CSI: Channel Estimation, Analysis and Optimization

This paper investigates the reconfigurable reflecting surface (RIS)-aided multiple-input-single-output (MISO) systems with imperfect channel state information (CSI), where RISrelated channels are modeled by Rician fading. Considering the overhead and complexity in practical systems, we employ the low-complexity maximum ratio combining (MRC) beamforming at the base station (BS), and configure the phase shifts of the RIS based on long-term statistical CSI. Specifically, we first estimate the overall channel matrix based on the linear minimum mean square error (LMMSE) estimator, and evaluate the performance of MSE and normalized MSE (NMSE). Then, with the estimated channel, we derive the closed-form expressions of the ergodic rate. The derived expressions show that with Rician RIS-related channels, the rate can maintain at a non-zero value when the transmit power is scaled down proportionally to 1/M or 1/N, where M and N are the number of antennas and reflecting elements, respectively. However, if all the RIS-related channels are fully Rayleigh, the transmit power of each user can only be scaled down proportionally to 1/ √ M or 1/N . Finally, numerical results verify the promising benefits from the RIS to traditional MISO systems.

[1]  Symeon Chatzinotas,et al.  Intelligent Reflecting Surface-assisted MU-MISO Systems with Imperfect Hardware: Channel Estimation, Beamforming Design , 2021, ArXiv.

[2]  Qingqing Wu,et al.  Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming , 2018, IEEE Transactions on Wireless Communications.

[3]  Derrick Wing Kwan Ng,et al.  Distributed IRS With Statistical Passive Beamforming for MISO Communications , 2020, IEEE Wireless Communications Letters.

[4]  Kezhi Wang,et al.  Power Scaling Law Analysis and Phase Shift Optimization of RIS-Aided Massive MIMO Systems With Statistical CSI , 2020, IEEE Transactions on Communications.

[5]  Lajos Hanzo,et al.  Intelligent Reflecting Surface Aided MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer , 2019, IEEE Journal on Selected Areas in Communications.

[6]  Shi Jin,et al.  Large Intelligent Surface-Assisted Wireless Communication Exploiting Statistical CSI , 2018, IEEE Transactions on Vehicular Technology.

[7]  Erik G. Larsson,et al.  Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems , 2011, IEEE Transactions on Communications.

[8]  Shuguang Cui,et al.  Channel Estimation for Intelligent Reflecting Surface Assisted Multiuser Communications: Framework, Algorithms, and Analysis , 2019, IEEE Transactions on Wireless Communications.

[9]  Emil Björnson,et al.  Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency , 2018, Found. Trends Signal Process..

[10]  Anas Chaaban,et al.  Intelligent Reflecting Surface Assisted MISO Downlink: Channel Estimation and Asymptotic Analysis , 2022 .

[11]  Lajos Hanzo,et al.  Multicell MIMO Communications Relying on Intelligent Reflecting Surfaces , 2019, IEEE Transactions on Wireless Communications.

[12]  H. Vincent Poor,et al.  Two-Timescale Design for Reconfigurable Intelligent Surface-Aided Massive MIMO Systems with Imperfect CSI , 2021, ArXiv.

[13]  Xiaohu You,et al.  Reconfigurable Intelligent Surfaces for 6G Systems: Principles, Applications, and Research Directions , 2021, IEEE Communications Magazine.

[14]  Kezhi Wang,et al.  Statistical CSI-Based Design for Reconfigurable Intelligent Surface-Aided Massive MIMO Systems With Direct Links , 2020, IEEE Wireless Communications Letters.