A Semi-Deterministic Channel Estimation Approach based on Geospatial Data and Fuzzy c-Means

This paper presents a semi-deterministic groupwise channel estimation method to generate UT-group CSI of user terminal (UT) zones in the service area for the angular-based hybrid precoding (AB-HP) in multi-user massive multiple-input multiple-output (MU-mMIMO) systems based on geospatial data and the fuzzy c-Means (FCM) clustering algorithm. The slow time-varying UT-level channel state information (CSI) between the base station (BS) and all possible UTs are generated by a ray tracing algorithm and grouped into clusters by a proposed FCM clustering. The service area is then divided into a number of non-overlapping UT zones, where each is characterized by a corresponding set of clusters used as UT-group CSI for RF beamformer to eliminate the required large online CSI acquisition overhead. Simulations are performed in both outdoor and indoor scenarios to evaluate the performance of the proposed channel estimation approach. Illustrative results show that the proposed method identifies clusters robust to imprecise UT-level CSI and provides RF beamformer with the UT-group CSI for different UT zones in the service area. Meanwhile, with the UT-group CSI, the AB-HP can successfully achieve a comparable sum-rate performance as the fully-digital precoding (FDP) system for UTs in specific zones without large dimensional CSI overhead.