TY  - EJOU
AU  - Markkandan, S. 
AU  - Sivasubramanian, S. 
AU  - Mulerikkal, Jaison 
AU  - Shaik, Nazeer 
AU  - Jackson, Beulah 
AU  - Naryanan, Lakshmi 

TI  - Massive MIMO Codebook Design Using Gaussian Mixture Model Based Clustering
T2  - Intelligent Automation \& Soft Computing

PY  - 2022
VL  - 32
IS  - 1
SN  - 2326-005X

AB  - 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.
KW  - Gaussian Mixture Model (GMM) based clustering; Massive MIMO; Codebook design; DFT

DO  - 10.32604/iasc.2022.021779