Hybrid Beamforming Design Based On Unsupervised Machine Learning For Millimeter Wave Systems

This article proposes a hybrid beamforming design with reduced channel state information (CSI) feedback. We use a beam sweeping procedure to provide channel measurements and feed a CSI report scheme. Thereby, the base station (BS) can perform an adequate estimation of the channel characteristics with reduced signaling overhead. Consequently, we need short pilot sequences and very few precoding matrix indicators (PMIs) to properly describe channel behavior. Moreover, we also evaluate different user selection strategies based on unsupervised machine learning framework that exploits the channel information provided by the proposed beam sweeping scheme. Our performance evaluation indicates that the user selection based on fuzzy c-means is able to efficiently explore the reduced CSI. The proposed hybrid beamforming scheme reduces the multi-user interference and achieves significant gains in total data rate as channel conditions and interference environment becomes more challenging.