  Hybrid beamforming is widely recognized as an important technique for
millimeter wave (mmWave) multiple input multiple output (MIMO) systems.
Generalized spatial modulation (GSM) is further introduced to improve the
spectrum efficiency. However, most of the existing works on beamforming assume
the perfect channel state information (CSI), which is unrealistic in practical
systems. In this paper, joint optimization of downlink pilot training, channel
estimation, CSI feedback, and hybrid beamforming is considered in GSM aided
frequency division duplexing (FDD) mmWave MIMO systems. With the help of deep
learning, the GSM hybrid beamformers are designed via unsupervised learning in
an end-to-end way. Experiments show that the proposed multi-resolution network
named GsmEFBNet can reach a better achievable rate with fewer feedback bits
compared with the conventional algorithm.
