scholarly journals Spatial-Temporal-DBSCAN-Based User Clustering and Power Allocation for Sum Rate Maximization in Millimeter-Wave NOMA Systems

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1854
Author(s):  
Huu-Trung Hoang ◽  
Quoc-Viet Pham ◽  
Won-Joo Hwang
Keyword(s):  
Power Allocation ◽  
Millimeter Wave ◽  
System Performance ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Clustering Algorithms ◽  
Real Life ◽  
Multiple User ◽  
User Clustering ◽  
Sum Rate

The combination of millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA) systems exploits the capability to serve multiple user devices simultaneously in one resource block. User clustering, power allocation (PA), and hybrid beamforming problems in mmWave-NOMA systems can utilize the network setting’s potential to enhance the system performance. Based on similar characteristics of the spatial distributions of users in real life, we propose a novel spatial-temporal density-based spatial clustering of applications with noise (ST-DBSCAN)-based unsupervised user clustering in order to enhance the system sum-rate. ST-DBSCAN is a state-of-the-art density-based clustering algorithm for solving spatial and non-spatial problems. Moreover, instead of symmetric PA, we propose an inter-cluster PA algorithm. Next, we apply boundary-compressed particle swarm optimization in order to reduce inter-cluster interference and enhance system performance. The simulation results reveal that our proposed solution improves the sum-rate of mmWave-NOMA-based systems when compared with that of mmWave-OMA-based systems. In addition, we compare our proposed algorithm with other benchmark user clustering algorithms in order to investigate the performance of our ST-DBSCAN-based user clustering algorithm. The results also illustrate that our proposed approach outperforms the state-of-the-art user clustering algorithms in mmWave-NOMA systems.

Joint user clustering and salp based particle swarm optimization algorithm for power allocation in MIMO-NOMA

2021 ◽  
Vol 40 (5) ◽  
pp. 9007-9019
Author(s):  
Jyotirmayee Subudhi ◽  
P. Indumathi
Keyword(s):  
Energy Efficiency ◽  
Particle Swarm Optimization ◽  
Power Allocation ◽  
Multiple Access ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Allocation Algorithm ◽  
User Clustering ◽  
Sum Rate

Non-Orthogonal Multiple Access (NOMA) provides a positive solution for multiple access issues and meets the criteria of fifth-generation (5G) networks by improving service quality that includes vast convergence and energy efficiency. The problem is formulated for maximizing the sum rate of MIMO-NOMA by assigning power to multiple layers of users. In order to overcome these problems, two distinct evolutionary algorithms are applied. In particular, the recently implemented Salp Swarm Algorithm (SSA) and the prominent Optimization of Particle Swarm (PSO) are utilized in this process. The MIMO-NOMA model optimizes the power allocation by layered transmission using the proposed Joint User Clustering and Salp Particle Swarm Optimization (PPSO) power allocation algorithm. Also, the closed-form expression is extracted from the current Channel State Information (CSI) on the transmitter side for the achievable sum rate. The efficiency of the proposed optimal power allocation algorithm is evaluated by the spectral efficiency, achievable rate, and energy efficiency of 120.8134bits/s/Hz, 98Mbps, and 22.35bits/Joule/Hz respectively. Numerical results have shown that the proposed PSO algorithm has improved performance than the state of art techniques in optimization. The outcomes on the numeric values indicate that the proposed PSO algorithm is capable of accurately improving the initial random solutions and converging to the optimum.

scholarly journals Canonical PSO Based K-Means Clustering Approach for Real Datasets

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Lopamudra Dey ◽  
Sanjay Chakraborty
Keyword(s):  
Data Mining ◽  
Air Pollution ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Cluster Validity ◽  
Validity Assessment ◽  
Different Types ◽  
Clustering Approach ◽  
Validity Measure

“Clustering” the significance and application of this technique is spread over various fields. Clustering is an unsupervised process in data mining, that is why the proper evaluation of the results and measuring the compactness and separability of the clusters are important issues. The procedure of evaluating the results of a clustering algorithm is known as cluster validity measure. Different types of indexes are used to solve different types of problems and indices selection depends on the kind of available data. This paper first proposes Canonical PSO based K-means clustering algorithm and also analyses some important clustering indices (intercluster, intracluster) and then evaluates the effects of those indices on real-time air pollution database, wholesale customer, wine, and vehicle datasets using typical K-means, Canonical PSO based K-means, simple PSO based K-means, DBSCAN, and Hierarchical clustering algorithms. This paper also describes the nature of the clusters and finally compares the performances of these clustering algorithms according to the validity assessment. It also defines which algorithm will be more desirable among all these algorithms to make proper compact clusters on this particular real life datasets. It actually deals with the behaviour of these clustering algorithms with respect to validation indexes and represents their results of evaluation in terms of mathematical and graphical forms.

A Dynamic Genetic Algorithm for Clustering Problems

2013 ◽  
Vol 411-414 ◽  
pp. 1884-1893
Author(s):  
Yong Chun Cao ◽  
Ya Bin Shao ◽  
Shuang Liang Tian ◽  
Zheng Qi Cai
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Search Space ◽  
Adaptive Mutation ◽  
Data Sets ◽  
Data Set ◽  
Local Optima ◽  
Clustering Problems

Due to many of the clustering algorithms based on GAs suffer from degeneracy and are easy to fall in local optima, a novel dynamic genetic algorithm for clustering problems (DGA) is proposed. The algorithm adopted the variable length coding to represent individuals and processed the parallel crossover operation in the subpopulation with individuals of the same length, which allows the DGA algorithm clustering to explore the search space more effectively and can automatically obtain the proper number of clusters and the proper partition from a given data set; the algorithm used the dynamic crossover probability and adaptive mutation probability, which prevented the dynamic clustering algorithm from getting stuck at a local optimal solution. The clustering results in the experiments on three artificial data sets and two real-life data sets show that the DGA algorithm derives better performance and higher accuracy on clustering problems.

scholarly journals Adaptive Initialization Method for K-Means Algorithm

2021 ◽  
Vol 4 ◽  
Author(s):  
Jie Yang ◽  
Yu-Kai Wang ◽  
Xin Yao ◽  
Chin-Teng Lin
Keyword(s):  
Time Complexity ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Superior Performance ◽  
Local Optima ◽  
Initial Cluster ◽  
Higher Dimensional ◽  
Real World Datasets ◽  
Random Method

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. In this research, we propose an adaptive initialization method for the K-means algorithm (AIMK) which can adapt to the various characteristics in different datasets and obtain better clustering performance with stable results. For larger or higher-dimensional datasets, we even leverage random sampling in AIMK (name as AIMK-RS) to reduce the time complexity. 22 real-world datasets were applied for performance comparisons. The experimental results show AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Specifically, AIMK-RS can significantly reduce the time complexity to O (n). Moreover, we exploit AIMK to initialize K-medoids and spectral clustering, and better performance is also explored. The above results demonstrate superior performance and good scalability by AIMK or AIMK-RS. In the future, we would like to apply AIMK to more partition-based clustering algorithms to solve real-life practical problems.

scholarly journals Efficient exploratory clustering analyses in large-scale exploration processes

2021 ◽  
Author(s):  
Manuel Fritz ◽  
Michael Behringer ◽  
Dennis Tschechlov ◽  
Holger Schwarz
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Clustering Algorithms ◽  
Search Space ◽  
Large Datasets ◽  
Search Spaces ◽  
Multiple Challenges ◽  
The One

AbstractClustering is a fundamental primitive in manifold applications. In order to achieve valuable results in exploratory clustering analyses, parameters of the clustering algorithm have to be set appropriately, which is a tremendous pitfall. We observe multiple challenges for large-scale exploration processes. On the one hand, they require specific methods to efficiently explore large parameter search spaces. On the other hand, they often exhibit large runtimes, in particular when large datasets are analyzed using clustering algorithms with super-polynomial runtimes, which repeatedly need to be executed within exploratory clustering analyses. We address these challenges as follows: First, we present LOG-Means and show that it provides estimates for the number of clusters in sublinear time regarding the defined search space, i.e., provably requiring less executions of a clustering algorithm than existing methods. Second, we demonstrate how to exploit fundamental characteristics of exploratory clustering analyses in order to significantly accelerate the (repetitive) execution of clustering algorithms on large datasets. Third, we show how these challenges can be tackled at the same time. To the best of our knowledge, this is the first work which simultaneously addresses the above-mentioned challenges. In our comprehensive evaluation, we unveil that our proposed methods significantly outperform state-of-the-art methods, thus especially supporting novice analysts for exploratory clustering analyses in large-scale exploration processes.

scholarly journals A comparative analysis of selected clustering algorithms for criminal profiling

2020 ◽  
Vol 39 (2) ◽  
pp. 464-471
Author(s):  
J.A. Adeyiga ◽  
S.O. Olabiyisi ◽  
E.O. Omidiora
Keyword(s):  
Expectation Maximization ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Expectation Maximization Algorithm ◽  
Real Life ◽  
Criminal Activity ◽  
Law Enforcement Agencies ◽  
Criminal Profiling ◽  
Hard Clustering ◽  
Membership Value

Several criminal profiling systems have been developed to assist the Law Enforcement Agencies in solving crimes but the techniques employed in most of the systems lack the ability to cluster criminal based on their behavioral characteristics. This paper reviewed different clustering techniques used in criminal profiling and then selects one fuzzy clustering algorithm (Expectation Maximization) and two hard clustering algorithm (K-means and Hierarchical). The selected algorithms were then developed and tested on real life data to produce "profiles" of criminal activity and behavior of criminals. The algorithms were implemented using WEKA software package. The performance of the algorithms was evaluated using cluster accuracy and time complexity. The results show that Expectation Maximization algorithm gave a 90.5% clusters accuracy in 8.5s, while K-Means had 62.6% in 0.09s and Hierarchical with 51.9% in 0.11s. In conclusion, soft clustering algorithm performs better than hard clustering algorithm in analyzing criminal data. Keywords: Clustering Algorithm, Profiling, Crime, Membership value

scholarly journals Hybrid Multi-User Precoding with Manifold Discriminative Learning for Millimeter-Wave Massive MIMO Systems

2021 ◽  
pp. 411-422
Author(s):  
Xiaoping Zhou ◽  
◽  
Bin Wang ◽  
Jing Zhang ◽  
Qian Zhang ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Mimo Systems ◽  
Gradient Methods ◽  
Maximization Problem ◽  
High Signal ◽  
User Clustering ◽  
Rate Maximization ◽  
Sum Rate Maximization ◽  
Sum Rate ◽  
Low Dimensional

In large-array millimeter-wave (mmWave) systems, hybrid multi-user precoding is one of the most attractive research topics. This paper first presents a low-dimensional manifolds architecture for the analog precoder. An objective function is formulated to maximize the Energy Efficiency (EE) in consideration of the insertion loss for hybrid multi-user precoder. The optimal scheme is intractable to achieve, so that we present a user clustering hybrid precoding scheme. By modeling each user set as a manifold, we formulate the problem as clustering-oriented multi-manifolds learning. We discuss the effect of non-ideal factors on the EE performance. Through proper user clustering, the hybrid multi-user precoding is investigated for the sum-rate maximization problem by manifold quasi conjugate gradient methods. The high signal to interference plus noise ratio (SINR) is achieved and the computational complexity is reduced by avoiding the conventional schemes to deal with high-dimensional channel parameters. Performance evaluations show that the proposed scheme can obtain near-optimal sum-rate and considerably higher spectral efficiency than some existing solutions.

Rough ISODATA Algorithm

2013 ◽  
Vol 3 (4) ◽  
pp. 1-14 ◽  
Author(s):  
S. Sampath ◽  
B. Ramya
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Real Life ◽  
Vital Role ◽  
Data Sets ◽  
Clustering Method ◽  
Data Set ◽  
Number Of Clusters ◽  
Real Life Data ◽  
Nonparametric Statistical

Cluster analysis is a branch of data mining, which plays a vital role in bringing out hidden information in databases. Clustering algorithms help medical researchers in identifying the presence of natural subgroups in a data set. Different types of clustering algorithms are available in the literature. The most popular among them is k-means clustering. Even though k-means clustering is a popular clustering method widely used, its application requires the knowledge of the number of clusters present in the given data set. Several solutions are available in literature to overcome this limitation. The k-means clustering method creates a disjoint and exhaustive partition of the data set. However, in some situations one can come across objects that belong to more than one cluster. In this paper, a clustering algorithm capable of producing rough clusters automatically without requiring the user to give as input the number of clusters to be produced. The efficiency of the algorithm in detecting the number of clusters present in the data set has been studied with the help of some real life data sets. Further, a nonparametric statistical analysis on the results of the experimental study has been carried out in order to analyze the efficiency of the proposed algorithm in automatic detection of the number of clusters in the data set with the help of rough version of Davies-Bouldin index.

Efficient Semi-Supervised Learning and Sparse Structural Learning for Feature Selection of Leukemia Dataset

2020 ◽  
Vol 10 (8) ◽  
pp. 1815-1824
Author(s):  
S. Nithya Roopa ◽  
N. Nagarajan
Keyword(s):  
Feature Selection ◽  
Supervised Learning ◽  
Health Informatics ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Research Work ◽  
Real Life ◽  
Support Vector ◽  
Structural Learning ◽  
Huge Amount

The amount of data produced in health informatics growing large and as a result analysis of this huge amount of data requires a great knowledge which is to be gained. The basic aim of health informatics is to take in real world medical data from all levels of human existence to help improve our understanding of medicine and medical practices. Huge amount of unlabeled data are obtainable in lots of real-life data-mining tasks, e.g., uncategorized messages in an automatic email categorization system, unknown genes functions for doing gene function calculation, and so on. Labelled data is frequently restricted and expensive to produce, while labelling classically needs human proficiency. Consequently, semi-supervised learning has become a topic of significant recent interest. This research work proposed a new semi-supervised grouping, where the performance of unsupervised clustering algorithms is enhanced with restricted numbers of supervision in labels form on constraints or data. The previous system designed a Clustering Guided Hybrid support vector machine based Sparse Structural Learning (CGHSSL) for feature selection. However, it does not produce a satisfactory accuracy results. In this research, proposed clustering-guided with Convolution Neural Network (CNN) based sparse structural learning clustering algorithm. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is progressed for learning cluster labels of input samples having more accuracy guiding features election at same time. Concurrently, prediction of cluster labels is as well performed by CNN by means of using hidden structure which is shared by various characteristics. The parameters of CNN are then optimized maximizing Multi-objective Bee Colony (MBO) algorithm that can unravel feature correlations to render outcomes with additional consistency. Row-wise sparse designs are then balanced to yield design depicted to suit for feature selection. This semi supervised algorithm is utilized to choose important characteristics from Leukemia1 dataset additional resourcefully. Therefore dataset size is decreased significantly utilizing semi supervised algorithm prominently. As well proposed Semi Supervised Clustering-Guided Sparse Structural Learning (SSCGSSL) technique is utilized to increase the clustering performance in higher. The experimental results show that the proposed system achieves better performance compared with the existing system in terms of Accuracy, Entropy, Purity, Normalized Mutual Information (NMI) and F-measure.

Stackelberg Game for User Clustering and Power Allocation in Millimeter Wave-NOMA Systems

2019 ◽  
Vol 18 (5) ◽  
pp. 2842-2857 ◽  
Author(s):  
Kaidi Wang ◽  
Jingjing Cui ◽  
Zhiguo Ding ◽  
Pingzhi Fan
Keyword(s):  
Power Allocation ◽  
Millimeter Wave ◽  
Stackelberg Game ◽  
User Clustering
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