Deep learning-based massive MIMO channel estimation with reduced feedback.

The downlink channel state information (CSI) must be available on the base station (BS) side to take advantage of all the features of massive multiple-input multiple-output (MIMO) systems. The channel estimation in massive MIMO systems is a challenging task because of the huge pilot and feedback overhead due to the large size of antennas. In this paper, we have proposed a multi task deep network for the channel estimation with the aim of decreasing the pilot and feedback overhead. An encoder network is designed to compress the received signal and reduce the feedback overhead. Furthermore, a decoder network is developed to reconstruct the compressed feedback. The estimator network is suggested to provide the channel estimation from the reconstructed feedback. The performance of the presented scheme has been evaluated in various simulation scenarios. The results confirm that the proposed method is capable of estimating the channel more accurately than the contemporary works. Moreover, this method has reduced the feedback and pilot overhead to a great extent.