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.

翻译：混合波束成形被广泛认为是毫米波多输入多输出系统中的一项重要技术。为进一步提升频谱效率，引入了广义空间调制。然而，现有关于波束成形的研究大多假设完美信道状态信息，这在实际系统中并不现实。本文针对GSM辅助的频分双工毫米波MIMO系统，联合优化了下行导频训练、信道估计、CSI反馈及混合波束成形。借助深度学习，通过无监督学习以端到端方式设计了GSM混合波束成形器。实验表明，所提出的多分辨率网络GsmEFBNet能够在更少反馈比特数下，相比传统算法获得更优的可达速率。