This paper presents a novel channel estimation technique for the multi-user massive multiple-input multiple-output (MU-mMIMO) systems using angular-based hybrid precoding (AB-HP). The proposed channel estimation technique generates group-wise channel state information (CSI) of user terminal (UT) zones in the service area by deep neural networks (DNN) and fuzzy c-Means (FCM) clustering. The slow time-varying CSI between the base station (BS) and feasible UT locations in the service area is calculated from the geospatial data by offline ray tracing and a DNN-based path estimation model associated with the 1-dimensional convolutional neural network (1D-CNN) and regression tree ensembles. Then, the UT-level CSI of all feasible locations is grouped into clusters by a proposed FCM clustering. Finally, the service area is divided into a number of non-overlapping UT zones. Each UT zone is characterized by a corresponding set of clusters named as UT-group CSI, which is utilized in the analog RF beamformer design of AB-HP to reduce the required large online CSI overhead in the MU-mMIMO systems. Then, the reduced-size online CSI is employed in the baseband (BB) precoder of AB-HP. Simulations are conducted in the indoor scenario at 28 GHz and tested in an AB-HP MU-mMIMO system with a uniform rectangular array (URA) having 16x16=256 antennas and 22 RF chains. Illustrative results indicate that 91.4% online CSI can be reduced by using the proposed offline channel estimation technique as compared to the conventional online channel sounding. The proposed DNN-based path estimation technique produces same amount of UT-level CSI with runtime reduced by 65.8% as compared to the computationally expensive ray tracing.

翻译：本文展示了使用基于角基复合混合编码(AB-HP)的多用户大规模多输出多输出多输出(MU-MIMIMO)系统的新型频道估算技术。拟议频道估算技术通过深神经网络(DNN)和模糊的C-Means(FCM)群集,生成服务区内用户终端(UT)区的分组频道状态信息。基础站(BS)与服务区内可行的UT地点之间时间变化缓慢的CSI(MU-MIMIM)(根据地理空间数据,通过离线跟踪和DNN(D-MIMU)路径估算模型,与1维调调调调调调混合混合混合混合混合混合混合混合混合混合混合混合(AB-AB)系统(1D-CNN)和回缩树组合。随后,所有可行地点的UTS-C-CSI(C-NFMM)分组分组信息(C-MUMI)数据分组分析,以UT-C-CSI(UT-C-C-SI)为对应的一组阵列数组数据,由C-MIS-MIFSILMI(C-MI-I-I-IL),由C-S-IL-IL AS-S-S-I(在AS-I IMF IM IML) IML IM IML) IML 数据基调调调调调调调调降后,由C-S-S-S-S-S-S-S-S-S-T-IDVDVDVDMLT-IDML 数据流数据,由C-ID-ID-S-S-S-S-S-S-S-I 数据,由C-T-T-S-S-L-S-I 数据流数据流数据,由AS-T-LVD-L-LTFDFD-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-L-I 进行。