Spatially Correlated RIS-Aided Secure Massive MIMO Under CSI and Hardware Imperfections
<|reference_start|>Spatially Correlated RIS-Aided Secure Massive MIMO Under CSI and Hardware Imperfections: 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 $N\rightarrow\infty$. 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.<|reference_end|>
@article{yang2024spatially,
    title={Spatially Correlated RIS-Aided Secure Massive MIMO Under CSI and
  Hardware Imperfections},
    author={Dan Yang, Jindan Xu, Wei Xu, Bin Sheng, Xiaohu You, Chau Yuen, Marco
  Di Renzo},
    journal={arXiv preprint arXiv:2404.05239},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05239},
    primaryClass={cs.IT math.IT}
}