Unsupervised Machine Learning-Based User Clustering in THz-NOMA Systems

In this letter, different unsupervised machine learning (ML)-based user clustering algorithms, including K-Means, agglomerative hierarchical clustering (AHC), and density-based spatial clustering of applications with noise (DBSCAN) are applied in non-orthogonal multiple access (NOMA) assisted terahertz (THz) networks. The key contribution of this letter is to design ML-based approaches to ensure that the secondary users can be clustered without knowing the number of clusters and degrading the performance of the primary users. The studies carried out in this letter show that the proposed schemes based on AHC and DBSCAN can achieve superior performance on system throughput and connectivity compared to the traditional clustering strategy, i.e., K-means, where the number of clusters is determined in an adaptive and automatic manner.<br/

User Clustering for Coexistence between Near-field and Far-field Communications

This letter investigates the coexistence between near-field (NF) and far-field (FF) communications, where multiple FF users are clustered to be served on the beams of legacy NF users, via non-orthogonal multiple access (NOMA). Three different successive interference cancellation (SIC) decoding strategies are proposed and a sum rate maximization problem is formulated to optimize the assignment and decoding order. The beam allocation problem is further reformulated as an overlapping coalitional game, which facilitates the the design of the proposed clustering algorithm. The optimal decoding order in each cluster is also derived, which can be integrated into the proposed clustering. Simulation results demonstrate that the proposed clustering algorithm is able to significantly improve the sum rate of the considered system, and the developed strategies achieve different trade-offs between sum rate and fairness

Verification and Validation of a Low-Mach-Number Large-Eddy Simulation Code against Manufactured Solutions and Experimental Results

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).To investigate turbulent reacting flows, a low-Mach number large-eddy simulation (LES) code called ‘LESsCoal’ has been developed in our group. This code employs the Germano dynamic sub-grid scale (SGS) model and the steady flamelet/progress variable approach (SFPVA) on a stagger-structured grid, in both time and space. The method of manufactured solutions (MMS) is used to investigate the convergence and the order of accuracy of the code when no model is used. Finally, a Sandia non-reacting propane jet and Sandia Flame D are simulated to inspect the performance of the code under experimental setups. The results show that MMS is a promising tool for code verification and that the low-Mach-number LES code can accurately predict the non-reacting and reacting turbulent flows. The validated LES code can be used in numerical investigations on the turbulent combustion characteristics of new fuel gases in the future.Peer reviewedFinal Published versio