Downlink Channel Estimation with Limited Feedback for FDD Multi-User Massive MIMO with Spatial Channel Correlation

Massive multiple input multiple output (MIMO) systems are a promising technology for next generation wireless communications due to their ability to increase capacity and enhance both spectrum and energy efficiency. To utilize the benefit of massive MIMO systems, accurate downlink channel state information at the transmitter (CSIT) is essential. Conventional approaches to obtain CSIT for frequency-division duplex (FDD) multi-user massive MIMO systems require downlink training and uplink CSI feedback. However, such training results in large overhead for massive MIMO systems because of the large dimensionality of the channel matrix. In this paper, we investigate the channel estimation problem in FDD multi-user massive MIMO systems with spatially correlated channels and develop an efficient channel estimation algorithm that exploits the sparsity structure of the downlink channel matrix. The proposed algorithm selects the best features from the measurement matrix to obtain efficient CSI acquisition that can reduce the downlink training overhead compared with the conventional LS/MMSE channel estimators. We compare the performance of our proposed channel estimation method with traditional ones in terms of normalized mean square error (MSE). Simulation results verify that the proposed algorithm can significantly reduce the pilot overhead and has better performance compared with the traditional channel estimation methods.