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A closed-loop training approach for massive MIMO beamforming systems

There has been a growing interest in wireless systems that employ a very large number of transmit antennas. Some theoretical results have shown that substantial improvements in network capacity are possible. Despite this work, a major challenge is how these large transmit arrays should perform training to allow receiver channel estimation. Without new techniques, the heavy burden of training could overwhelm the system and mitigate any possible improvements, especially in systems using frequency division duplexing (FDD) where channel reciprocity cannot be exploited. In this work, we propose the use of closed-loop training. In this framework, the transmitted training signal is optimized to improve data communications performance by using prior information about the current channel obtained from past channel estimates. The work focuses on block-fading channels with temporal and spatial correlation. Simulation results show improved performance.