Randomized Approximate Channel Estimator in Massive-MIMO Communication

Massive MIMO is considered as one key enabling technology in 5G communications. Although the high-quality channel state information (CSI) estimation is essential to improve the energy/spectrum efficiency of massive MIMO systems, the optimal MMSE estimator unfortunately creates one major challenge, due to its formidable computation complexity. In this letter, we propose a rank-restrained low-complexity MMSE channel estimator for massive MIMO communications, leveraging a novel concept of randomized low-rank approximation. To accomplish this, we first design a two-stage pilot training scheme. Rather than directly estimating the large channel matrix as usual, we then acquire two low-dimensional subsets of it, which respectively gives the row and column sampling version of unknown channel matrix. Finally, we reconstruct the complete channel matrix via such two estimated low-dimensional sketches, by exploiting the low-rank structure of channel. As demonstrated, our scheme significantly reduces the time complexity as well as the energy consumption in CSI acquisition, which yet attains the near-optimal estimation accuracy. It thus provides great promises to the practical deployment of massive MIMO communications.