TY - JOUR
T1 - Power Scaling Law Analysis and Phase Shift Optimization of RIS-aided Massive MIMO Systems with Statistical CSI
AU - Zhi, Kangda
AU - Pan, Cunhua
AU - Ren, Hong
AU - Wang, Kezhi
N1 - Funding information: This work was supported in part by the National Key Research and Development Project (2019YFE0123600), National Natural Science Foundation of China (62101128), Basic Research Project of Jiangsu Provincial Department of Science and Technology (BK20210205), and High Level Personal Project of Jiangsu Province (JSSCBS20210105). Kangda Zhi’s work was supported by China Scholarship Council.
PY - 2022/5
Y1 - 2022/5
N2 - This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system, where the phase shifts of the RIS are designed relying on statistical channel state information (CSI). Considering the complex environment, the general Rician channel model is adopted for both the users-RIS links and RIS-BS links. We first derive the closed-form approximate expressions for the achievable rate which holds for arbitrary numbers of base station (BS) antennas and RIS elements. Then, we utilize the derived expressions to provide some insights, including the asymptotic rate performance, the power scaling laws, and the impacts of various system parameters on the achievable rate. We also tackle the sum-rate maximization and the minimum user rate maximization problems by optimizing the phase shifts at the RIS based on genetic algorithm (GA). Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems. Our simulations also demonstrate the feasibility of deploying large-size but low-resolution RIS in massive MIMO systems.
AB - This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system, where the phase shifts of the RIS are designed relying on statistical channel state information (CSI). Considering the complex environment, the general Rician channel model is adopted for both the users-RIS links and RIS-BS links. We first derive the closed-form approximate expressions for the achievable rate which holds for arbitrary numbers of base station (BS) antennas and RIS elements. Then, we utilize the derived expressions to provide some insights, including the asymptotic rate performance, the power scaling laws, and the impacts of various system parameters on the achievable rate. We also tackle the sum-rate maximization and the minimum user rate maximization problems by optimizing the phase shifts at the RIS based on genetic algorithm (GA). Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems. Our simulations also demonstrate the feasibility of deploying large-size but low-resolution RIS in massive MIMO systems.
KW - Intelligent reflecting surface (IRS)
KW - massive MIMO
KW - reconfigurable intelligent surface (RIS)
KW - Rician fading channels
KW - statistical CSI
KW - uplink achievable rate
UR - http://www.scopus.com/inward/record.url?scp=85127498962&partnerID=8YFLogxK
U2 - 10.1109/TCOMM.2022.3162580
DO - 10.1109/TCOMM.2022.3162580
M3 - Article
AN - SCOPUS:85127498962
SN - 0090-6778
VL - 70
SP - 3558
EP - 3574
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
IS - 5
ER -