1-bit direction of arrival estimation based on Compressed Sensing

Massive MIMO plays an important role for future cellular networks since the large number of antenna elements is capable of increasing the spectral efficiency and the amount of usable spectrum. The 1-bit analog-to-digital converters can drastically reduce the resulting complexity and power consumption. Therefore, we investigate the Direction of Arrival (DoA) estimation using 1-bit measurements of many antenna elements in this paper. We extend Binary Iterative Hard Thresholding (BIHT), an efficient sparse recovery algorithm from the area of Compressed Sensing (CS) that takes the 1-bit quantization explicitly into account, to complex-valued signals and multiple measurement vectors such that it is applicable to 1-bit DoA estimation with multiple snapshots. The comparison of the resulting Complex-valued BIHT (CBIHT) algorithm to subspace- and CS-based methods in terms of both DoA estimation performance and computational complexity demonstrates that CBIHT is well suited for scenarios with many antenna elements and a few snapshots.