Open Access

ARTICLE

Massive MIMO Codebook Design Using Gaussian Mixture Model Based Clustering

by S. Markkandan^1,*, S. Sivasubramanian², Jaison Mulerikkal³, Nazeer Shaik⁴, Beulah Jackson⁵, Lakshmi Naryanan⁶

1 Department of ECE, SRM TRP Engineering College, Tamil Nadu, 621105, India
2 Dhanalakshmi College of Engineering, Chennai, 601301, Tamil Nadu, India
3 Department of Information 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:

Intelligent Automation & Soft Computing 2022, 32(1), 361-375. https://doi.org/10.32604/iasc.2022.021779

Received 14 July 2021; Accepted 26 August 2021; Issue published 26 October 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 clustering 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 centroids part of clustering has been utilized for constructing a codebook with statistic channel information, with an average distance that is the shortest towards the true channel data. The enhanced GMM based clustering codebook design outperforms traditional methods, particularly in the situations of non-uniform distribution of channels as demonstrated via simulation results which match theoretical analyses concerning achievable rate. The proposed GMM based clustering codebook design is compared with DFT-based clustering codebook design and k-means based clustering codebook design.

---

Keywords

---

Citations