TY - GEN
T1 - Analysis and optimization of RIS-aided massive MIMO systems with statistical CSI
AU - Zhi, Kangda
AU - Pan, Cunhua
AU - Zhou, Gui
AU - Ren, Hong
AU - Wang, Kezhi
N1 - Funding Information: This work was supported in part by the National Key Research and Development Project under Grant 2019YFE0123600, and the Research Fund of National Mobile Communications Research Laboratory, Southeast University (No.2021B01).
PY - 2021/7/28
Y1 - 2021/7/28
N2 - This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system with statistical channel state information (CSI). The RIS is deployed to help conventional massive MIMO networks serve the users in the dead zone. We consider the Rician channel model and exploit the long-time statistical CSI to design the phase shifts of the RIS, while the maximum ratio combination (MRC) technique is applied for the active beamforming at the base station (BS) relying on the instantaneous CSI. Firstly, we derive the closed-form expressions for the uplink achievable rate which holds for arbitrary numbers of base station (BS) antennas. Then, we propose a genetic algorithm (GA)-based method to maximize the minimum user rate by optimizing the phase shifts at the RIS. Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional massive MIMO systems.
AB - This paper considers an uplink reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) system with statistical channel state information (CSI). The RIS is deployed to help conventional massive MIMO networks serve the users in the dead zone. We consider the Rician channel model and exploit the long-time statistical CSI to design the phase shifts of the RIS, while the maximum ratio combination (MRC) technique is applied for the active beamforming at the base station (BS) relying on the instantaneous CSI. Firstly, we derive the closed-form expressions for the uplink achievable rate which holds for arbitrary numbers of base station (BS) antennas. Then, we propose a genetic algorithm (GA)-based method to maximize the minimum user rate by optimizing the phase shifts at the RIS. Finally, extensive simulations are provided to validate the benefits by integrating RIS into conventional 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=85116431967&partnerID=8YFLogxK
U2 - 10.1109/ICCCWorkshops52231.2021.9538874
DO - 10.1109/ICCCWorkshops52231.2021.9538874
M3 - Conference contribution
AN - SCOPUS:85116431967
T3 - 2021 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2021
SP - 153
EP - 158
BT - 2021 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2021
Y2 - 28 July 2021 through 30 July 2021
ER -