Spatio-Radio Resource Management and Hybrid Beamforming for Limited Feedback Massive MIMO Systems

• Main Authors: Hedi Khammari, Irfan Ahmed, Ghulam Bhatti, Masoud Alajmi
• Format: Article
• Language: English
• Published: MDPI AG 2019-09-01
• Series: Electronics
• Subjects: hybrid beamforming; massive MIMO; resource allocation
• Online Access: https://www.mdpi.com/2079-9292/8/10/1061

In this paper, a joint spatio&#8722;radio frequency resource allocation and hybrid beamforming scheme for the massive multiple-input multiple-output (MIMO) systems is proposed. We consider limited feedback two-stage hybrid beamformimg for decomposing the precoding matrix at the base-station. To reduce the channel state information (CSI) feedback of massive MIMO, we utilize the channel covariance-based RF precoding and beam selection. This beam selection process minimizes the inter-group interference. The regularized block diagonalization can mitigate the inter-group interference, but requires substantial overhead feedback. We use channel covariance-based eigenmodes and discrete Fourier transforms (DFT) to reduce the feedback overhead and design a simplified analog precoder. The columns of the analog beamforming matrix are selected based on the users&#8217; grouping performed by the K-mean unsupervised machine learning algorithm. The digital precoder is designed with joint optimization of intra-group user utility function. It has been shown that more than <inline-formula> <math display="inline"> <semantics> <mrow> <mn>50</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> feedback overhead is reduced by the eigenmodes-based analog precoder design. The joint beams, users scheduling and limited feedbacK-based hybrid precoding increases the sum-rate by <inline-formula> <math display="inline"> <semantics> <mrow> <mn>27.6</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> compared to the sum-rate of one-group case, and reduce the feedback overhead by <inline-formula> <math display="inline"> <semantics> <mrow> <mn>62.5</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> compared to the full CSI feedback.