Asymptotic CRB Analysis of Random RIS-Assisted Large-Scale Localization Systems.
CoRR(2023)
摘要
This paper studies the performance of a randomly RIS-assisted multi-target
localization system, in which the configurations of the RIS are randomly set to
avoid high-complexity optimization. We first focus on the scenario where the
number of RIS elements is significantly large, and then obtain the scaling law
of Cram\'er-Rao bound (CRB) under certain conditions, which shows that CRB
decreases in the third or fourth order as the RIS dimension increases. Second,
we extend our analysis to large systems where both the number of targets and
sensors is substantial. Under this setting, we explore two common RIS models:
the constant module model and the discrete amplitude model, and illustrate how
the random RIS configuration impacts the value of CRB. Numerical results
demonstrate that asymptotic formulas provide a good approximation to the exact
CRB in the proposed randomly configured RIS systems.
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