Yang, D., Xu, J., Xu, W., Sheng, B., You, X., Yuen, C., & Renzo, M. D. (2024). Spatially Correlated RIS-Aided Secure Massive MIMO Under CSI and Hardware Imperfections. IEEE Transactions on Wireless Communications, 23(9), 11461–11475. https://doi.org/10.1109/twc.2024.3382342
Abstract:
This paper investigates the integration of a reconfigurable
intelligent surface (RIS) into a secure multiuser massive
multiple-input multiple-output (MIMO) system in the presence
of transceiver hardware impairments (HWI), imperfect channel
state information (CSI), and spatially correlated channels.
We first introduce a linear minimum-mean-square error estimation
algorithm for the aggregate channel by considering the
impact of transceiver HWI and RIS phase-shift errors. Then,
we derive a lower bound for the achievable ergodic secrecy rate
in the presence of a multi-antenna eavesdropper when artificial
noise (AN) is employed at the base station (BS). In addition,
the obtained expressions of the ergodic secrecy rate are further
simplified in some noteworthy special cases to obtain valuable
insights. To counteract the effects of HWI, we present a power
allocation optimization strategy between the confidential signals
and AN, which admits a fixed-point equation solution. Our analysis
reveals that a non-zero ergodic secrecy rate is preserved if the total transmit power decreases no faster than 1/N, where N is
the number of RIS elements. Moreover, the ergodic secrecy rate
grows logarithmically with the number of BS antennas M and
approaches a certain limit in the asymptotic regime N → ∞.
Simulation results are provided to verify the derived analytical
results. They reveal the impact of key design parameters on the
secrecy rate. It is shown that, with the proposed power allocation
strategy, the secrecy rate loss due to HWI can be counteracted
by increasing the number of low-cost RIS elements.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Key Research and Development Program - NA
Grant Reference no. : 2022YFB2901600
This research / project is supported by the NSFC - NA
Grant Reference no. : 62211530108
This research / project is supported by the Fundamental Research Funds for the Central Universities - NA
Grant Reference no. : 2242022k60002, 2242023K5003
This research / project is supported by the Ministry of Education (MOE), Singapore - Academic Research Fund Tier 2
Grant Reference no. : Award MOE-T2EP50220-0019
This research is supported by core funding from: Science and Engineering Research Council of Agency for Science, Technology and Research (A*STAR), Singapore
Grant Reference no. : M22L1b0110
This research / project is supported by the European Commission - Horizon Europe Project COVER
Grant Reference no. : 101086228
This research / project is supported by the European Commission - Horizon Europe Project UNITE
Grant Reference no. : 101129618
This research / project is supported by the European Commission - Horizon Europe Project INSTINCT
Grant Reference no. : 101139161
This research / project is supported by the Agence Nationale de la Recherche (ANR) - France 2030 Project titled Agence nationale de la recherche (ANR)-Programmes et équipements prioritaires de recherche (PEPR) Networks of the Future
Grant Reference no. : NF-PERSEUS 22-PEFT-004
This research / project is supported by the CHIST-ERA - PASSIONATE
Grant Reference no. : CHIST-ERA-22-WAI-04, ANR-23-CHR4-0003-01