LAUSR

Receivers with one-bit analog-to-digital (ADC) converters are considered as a potential solution to reduce the power consumption and hardware complexity for millimeter wave (mmWave) multiple input multiple output systems. However, it is challenging to estimate the mmWave channel with one-bit ADCs because of significant distortions of received signal and a limited scattering nature. In this work, by exploiting the sparse and low-rank properties, we develop a mmWave channel estimation algorithm which can maximize the log-likelihoods under the rank and entry-wise infinity-norm constraints. Instead of using the convex relaxation for rank operation, matrix factorization is introduced to represent the exact rank constraint implicitly. The entry-wise infinity-norm is reformulated via the log-barrier method. The optimum estimation can be obtained iteratively via projected gradient ascent algorithm. Moreover, an upper bound performance of one-bit channel estimation is derived. Simulation results demonstrate that the proposed algorithm can obtain accurate estimation with relatively low computational complexity.