Advanced Codebook Generation Using High-Resolution k-Means Clustering to PMI Data

An enhanced codebook generation approach based only on precoding matrix indicator (PMI) feedback information is proposed. By utilizing the kernel density estimation (KDE) to produce higher-resolution distributions from discrete PMI data and applying the k-means clustering algorithm, the approach iteratively updates the set of beamforming vectors to autonomously adapt to the channel characteristics and user equipment (UE) positions. The proposed codebook is 3GPP standard compliant in the sense that it can be applied to existing systems to achieve significantly enhanced beamforming performance, without any alterations to reference signaling or channel measurement report.