Complex-Valued Binary Compressive Sensing

One-bit (or binary) compressive sensing (CS) is a relatively new idea in the theory of sparse signal reconstruction. It is based on using only the sign of the available measurements for the signal recovery. In this paper, we analyze the one-bit CS concepts on complex-valued random Gaussian measurement matrices. The signal is reconstructed using an iterative hard thresholding algorithm, modified for the complex-valued binary measurements. The considered CS approach is particularly suitable for hardware realizations. The reconstruction performance is validated numerically, and compared with the traditional CS reconstruction based on quantized digital measurements.