Massive MIMO Codebook Design Using Gaussian Mixture Model Based Clustering S. Markkandan 1,* , S. Sivasubramanian 2 , Jaison Mulerikkal 3 , Nazeer Shaik 4 , Beulah Jackson 5 and Lakshmi Naryanan 6 1 Department of ECE, SRM TRP Engineering College, Tamil Nadu, 621105, India 2 Dhanalakshmi College of Engineering, Chennai, 601301, Tamil Nadu, India 3 Department ofInformation Technology, Rajagiri School of Engineering and Technology, Kerala, India 4 Department of CSE, Bapatla Engineering College, Bapatla, 522102, India 5 Department of ECE, Saveetha Engineering College, Tamil Nadu, India 6 Gojan School of Business and Technology, Tamil Nadu, India *Corresponding Author: S. Markkandan. Email: markkandan@gmail.com Received: 14 July 2021; Accepted: 26 August 2021 Abstract: The codebook design is the most essential core technique in constrained feedback massive multi-input multi-output (MIMO) system communications. MIMO vectors have been generally isotropic or evenly distributed in traditional codebook designs. In this paper, Gaussian mixture model (GMM) based cluster- ing codebook design is proposed, which is inspired by the strong classification and analytical abilities of clustering techniques. Huge quantities of channel state information (CSI) are initially saved as entry data of the clustering process. Further, split into N number of clusters based on the shortest distance. The cen- troids part of clustering has been utilized for constructing a codebook with statis- tic channel information, with an average distance that is the shortest towards the true channel data. The enhanced GMM based clustering codebook design outper- forms traditional methods, particularly in the situations of non-uniform distribu- tion of channels as demonstrated via simulation results which match theoretical analyses concerning achievable rate. The proposed GMM based clustering code- book design is compared with DFT-based clustering codebook design and k-means based clustering codebook design. Keywords: Gaussian Mixture Model (GMM) based clustering; Massive MIMO; Codebook design; DFT 1 Introduction Massive multiple-input multiple-output (MIMO) is becoming the major driver for 5th generation wireless communication as well as transmission systems to enhance data speeds [1]. The precision of channel state information (CSI) has been important for beam development and spatial multiplexing improvements in transceivers with a huge number of antenna components [2]. The most popular approach for acquiring CSI is restricted feedback dependent on codebook, in which the receiver exclusively This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Intelligent Automation & Soft Computing DOI:10.32604/iasc.2022.021779 Article ech T Press Science