Weighted Bayesian Cram$\acute{\text{e}}$r-Rao Bound for Mixed-Resolution Parameter Estimation

Abstract

Mixed-resolution architectures, combining high-resolution (analog) data with coarsely quantized (e.g., 1-bit) data, are widely employed in emerging communication and radar systems to reduce hardware costs and power consumption. However, the use of coarsely quantized data introduces non-trivial tradeoffs in parameter estimation tasks. In this paper, we investigate the derivation of lower bounds for such systems. In particular, we develop the weighted Bayesian Cramer-Rao bound (WBCRB) for the mixed-resolution setting with a general weight function. We demonstrate the special cases of: (i) the classical BCRB; (ii) the WBCRB that is based on the Bayesian Fisher information matrix (BFIM)-Inverse weighting; and (iii) the Aharon-Tabrikian tightest WBCRB with an optimal weight function. Based on the developed WBCRB, we propose a new method to approximate the mean-squared-error (MSE) by partitioning the estimation problem into two regions: (a) where the 1-bit quantized data is informative; and (b) where it is saturated. We apply region-specific WBCRB approximations in these regions to achieve an accurate composite MSE estimate. We derive the bounds and MSE approximation for the linear Gaussian orthonormal (LGO) model, which is commonly used in practical signal processing applications. Our simulation results demonstrate the use of the proposed bounds and approximation method in the LGO model with a scalar unknown parameter. It is shown that the WBCRB outperforms the BCRB, where the BFIM-Inverse weighting version approaches the optimal WBCRB. Moreover, it is shown that the WBCRB-based MSE approximation is tighter and accurately predicts the non-monotonic behavior of the MSE in the presence of quantization errors.