Unsupervised Machine Intelligence for Automation of Multi-Dimensional Modulation

Abstract

In this letter, we propose a new unsupervised machine learning technique for a multi-dimensional modulator that can autonomously learn key exploitable features from significant variations of multi-dimensional wireless propagation parameters, followed by a real-time prediction of the best multi-dimensional modulation mode to be used for the next resilient transmission. The proposed method aims to embrace the potential of the unsupervised K-means clustering into the physical layer of noncoherent multi-dimensional transmission. Simulation results show that the proposed scheme can outperform the benchmarks at a cost of simple offline training.

Machine Learning-Assisted Adaptive Modulation for Optimized Drone-User Communication in B5G

“…In a similar work [22], the authors used the SVM technique to achieve channel and modulation selection in cognitive radio. In [24], the authors used the K-Nearest Neighbour (K-NN) algorithm to achieve adaptive modulation in the underwater acoustic network. However, in addition to K-NN, they used an unsupervised algorithm as a tool to condense the data set.…”

Federated K-Means Clustering for Adaptive OFDM-IM

“…K-means clustering can efficiently extract the implicit pattern of multi-dimensional vectors by clustering them according to the Euclidean distance between them. [4] proposed an OFDM-IM adaptation with the Xueyu Wu, Andy M. Tyrrell and Youngwook Ko are with the School of Physics, Engineering and Technology, University of York, United Kingdom (Emails: xueyu.wu@york.ac.uk, andy.tyrrell@york.ac.uk and youngwook.ko@york.ac.uk). use of single user k-means clustering.…”