scholarly journals Throughput Optimization Using Metaheuristic-Tabu Search in the Multicast D2D Communications Underlaying LTE-A Uplink Cellular Networks

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 440 ◽  
Author(s):  
Devarani Devi Ningombam ◽  
Seokjoo Shin
Keyword(s):  
Resource Allocation ◽  
Tabu Search ◽  
Resource Sharing ◽  
Cellular Network ◽  
Search Algorithm ◽  
D2d Communication ◽  
Fractional Frequency Reuse ◽  
Tabu Search Algorithm ◽  
Throughput Optimization ◽  
Uplink Interference

The sum throughput of a cellular network can be improved when nearby devices employ direct communications using a resource sharing technique. Multicast device-to-device (M-D2D) communication is a promising solution to accommodate higher transmission rates. In an M-D2D communication, a multicast group is formed by considering a transmitter that can transmit the same information to multiple receivers by considering the transmission link conditions. In this paper, we focus on the uplink interference generated due to the non-orthogonal sharing of resources between the cellular users and M-D2D groups. To mitigate the interference, we propose a spectrum reuse-based resource allocation and power control scheme for M-D2D communication underlaying an uplink cellular network. We formulate the throughput optimization problem by considering the fractional frequency reuse (FFR) method within a multicell cellular network. In addition, a metaheuristic-tabu search algorithm is developed that maximizes the probability of finding optimal solutions by minimizing uplink interference. To analyze fairness resource distribution among users, we finally consider Jain’s fairness index. Simulation results show that the proposed scheme can improve the coverage probability, success rate, spectral efficiency, and sum throughput of the network, compared with a random resource allocation scheme without a metaheuristic-tabu search algorithm.

scholarly journals Non-Orthogonal Resource Sharing Optimization for D2D Communication in LTE-A Cellular Networks: A Fractional Frequency Reuse-Based Approach

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 238 ◽  
Author(s):  
Devarani Devi Ningombam ◽  
Seokjoo Shin
Keyword(s):  
Resource Allocation ◽  
Cellular Networks ◽  
Resource Sharing ◽  
System Performance ◽  
Frequency Reuse ◽  
Spectrum Efficiency ◽  
D2d Communication ◽  
Traffic Load ◽  
Fractional Frequency Reuse ◽  
Fractional Frequency

To handle the fast-growing demand for high data rate applications, the capacity of cellular networks should be reinforced. However, the available radio resources in cellular networks are scarce, and their formulation is expensive. The state-of-the art solution to this problem is a new local networking technology known as the device-to-device (D2D) communication. D2D communications have great capability in achieving outstanding performance by reusing the existing uplink cellular channel resources. In D2D communication, two devices in close proximity can communicate directly without traversing data traffic through the evolved-NodeB (eNB). This results in a reduced traffic load to the eNB, reduced end-to-end delay, and improved spectral efficiency and system performance. However, enabling D2D communication in an LTE-Advanced (LTE-A) cellular network causes severe interference to traditional cellular users and D2D pairs. To maintain the quality of service (QoS) of the cellular users and D2D pairs and reduce the interference, we propose a distance-based resource allocation and power control scheme using fractional frequency reuse (FFR) technique. We calculate the system outage probability, total throughput and spectrum efficiency for both cellular users and D2D pairs in terms of their signal-to-interference-plus-noise ratio (SINR). Our simulation results show that the proposed scheme reduces interference significantly and improves system performance compared to the random resource allocation (RRA) and resource allocation (RA) without sectorization scheme.

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

An extreme‐point tabu‐search algorithm for fixed‐charge network problems

Networks ◽  
2021 ◽  
Vol 77 (2) ◽  
pp. 322-340 ◽  
Author(s):  
Richard S. Barr ◽  
Fred Glover ◽  
Toby Huskinson ◽  
Gary Kochenberger
Keyword(s):  
Tabu Search ◽  
Extreme Point ◽  
Search Algorithm ◽  
Fixed Charge ◽  
Tabu Search Algorithm ◽  
Network Problems

scholarly journals A support vector machine with the tabu search algorithm for freeway incident detection

2014 ◽  
Vol 24 (2) ◽  
pp. 397-404 ◽  
Author(s):  
Baozhen Yao ◽  
Ping Hu ◽  
Mingheng Zhang ◽  
Maoqing Jin
Keyword(s):  
Support Vector Machine ◽  
Tabu Search ◽  
Traffic Management ◽  
Search Algorithm ◽  
Detection System ◽  
Support Vector ◽  
Incident Detection ◽  
Tabu Search Algorithm ◽  
Proposed Model ◽  
Parameter Values

Abstract Automated Incident Detection (AID) is an important part of Advanced Traffic Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the influence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artificial Neural Networks (ANNs) in freeway incident detection.

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