Covariance based signal parameter estimation of coarse quantized signals

The use of low resolution analog-to-digital converters is an effective solution to reduce the complexity and the power consumption of the analog front-end, especially in the context of massive multiple-antenna and large system bandwidth. However, most well-known signal parameter estimation algorithms assume that the receiver has access to the observations data with infinite precision. In this paper, we investigate a method to improve the performance of covariance based algorithms that operate on quantized signals. To this end, we use a particular transformation between the input and output second order statistics when affected by non-linear processing. By applying this transformation, we could then utilize standard algorithms with almost no additional complexity. The introduced method is also of particular interest in the context of adaptive signal processing, since many adaptive processing algorithms are based on estimating the received signal covariance matrices. Through simulations, we show that the method is capable of improving the estimation performance especially in the extreme case of one-bit quantization.