TY - JOUR
T1 - Intelligent reflecting surface-assisted multi-user miso communication
T2 - Channel estimation and beamforming design
AU - Nadeem, Qurrat Ul Ain
AU - Alwazani, Hibatallah
AU - Kammoun, Abla
AU - Chaaban, Anas
AU - Debbah, Mérouane
AU - Alouini, Mohamed Slim
N1 - Publisher Copyright:
© 2020 IEEE. All rights reserved.
PY - 2020
Y1 - 2020
N2 - The concept of reconfiguring wireless propagation environments using intelligent reflecting surfaces (IRS)s has recently emerged, where an IRS comprises of a large number of passive reflecting elements that can smartly reflect the impinging electromagnetic waves for performance enhancement. Previous works have shown promising gains assuming the availability of perfect channel state information (CSI) at the base station (BS) and the IRS, which is impractical due to the passive nature of the reflecting elements. This paper makes one of the preliminary contributions of studying an IRS-assisted multi-user multiple-input single-output (MISO) communication system under imperfect CSI. Different from the few recent works that develop least-squares (LS) estimates of the IRS-assisted channel vectors, we exploit the prior knowledge of the large-scale fading statistics at the BS to derive the Bayesian minimum mean squared error (MMSE) channel estimates under a protocol in which the IRS applies a set of optimal phase shifts vectors over multiple channel estimation sub-phases. The resulting mean squared error (MSE) is both analytically and numerically shown to be lower than that achieved by the LS estimates. Joint designs for the precoding and power allocation at the BS and reflect beamforming at the IRS are proposed to maximize the minimum user signal-to-interference-plus-noise ratio (SINR) subject to a transmit power constraint. Performance evaluation results illustrate the efficiency of the proposed system and study its susceptibility to channel estimation errors.
AB - The concept of reconfiguring wireless propagation environments using intelligent reflecting surfaces (IRS)s has recently emerged, where an IRS comprises of a large number of passive reflecting elements that can smartly reflect the impinging electromagnetic waves for performance enhancement. Previous works have shown promising gains assuming the availability of perfect channel state information (CSI) at the base station (BS) and the IRS, which is impractical due to the passive nature of the reflecting elements. This paper makes one of the preliminary contributions of studying an IRS-assisted multi-user multiple-input single-output (MISO) communication system under imperfect CSI. Different from the few recent works that develop least-squares (LS) estimates of the IRS-assisted channel vectors, we exploit the prior knowledge of the large-scale fading statistics at the BS to derive the Bayesian minimum mean squared error (MMSE) channel estimates under a protocol in which the IRS applies a set of optimal phase shifts vectors over multiple channel estimation sub-phases. The resulting mean squared error (MSE) is both analytically and numerically shown to be lower than that achieved by the LS estimates. Joint designs for the precoding and power allocation at the BS and reflect beamforming at the IRS are proposed to maximize the minimum user signal-to-interference-plus-noise ratio (SINR) subject to a transmit power constraint. Performance evaluation results illustrate the efficiency of the proposed system and study its susceptibility to channel estimation errors.
KW - Alternating optimization
KW - Channel estimation
KW - Intelligent reflecting surface
KW - Minimum mean squared error
KW - Multiple-input single-output system
UR - http://www.scopus.com/inward/record.url?scp=85092795358&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092795358&partnerID=8YFLogxK
U2 - 10.1109/OJCOMS.2020.2992791
DO - 10.1109/OJCOMS.2020.2992791
M3 - Article
AN - SCOPUS:85092795358
SN - 2644-125X
VL - 1
SP - 661
EP - 680
JO - IEEE Open Journal of the Communications Society
JF - IEEE Open Journal of the Communications Society
M1 - 2992791
ER -