Optimal Radio Access Technology Selection Algorithm for LTE-WiFi Network

Roy, Arghyadip; Chaporkar, Prasanna; Karandikar, Abhay

doi:10.1109/tvt.2018.2805190

Cited by 36 publications

(14 citation statements)

References 30 publications

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“…erefore, it helps provide a high-quality weight vector for the GRA algorithm and obtain better ranking results [13]. Next, international ship and port facility security (ISPs) analyzes subscriber behavior to adjust service prices to maximize revenue [14].…”

Section: Current Status Of Researchmentioning

confidence: 99%

[Retracted] Load Balancing Selection Method and Simulation in Network Communication Based on AHP‐DS Heterogeneous Network Selection Algorithm

Xiao

2021

Complexity

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This article proposes an Analytic Hierarchy Process Dempster-Shafer (AHP-DS) and similarity-based network selection algorithm for the scenario of dynamic changes in user requirements and network environment; combines machine learning with network selection and proposes a decision tree-based network selection algorithm; combines multiattribute decision-making and genetic algorithm to propose a weighted Gray Relation Analysis (GRA) and genetic algorithm-based network access decision algorithm. Firstly, the training data is obtained from the collaborative algorithm, and it is used as the training set, and the network attributes are used as the attribute set, and the continuous attributes are discretized by dichotomization, and the attribute that can make the greatest information gain is selected as the division feature, and a decision tree with strong generalization ability is finally obtained, which is used as the decision basis for network access selection. The simulation results show that the algorithm proposed in this thesis can effectively improve user service quality under three services, and the algorithm is simple and effective with low complexity. It first uses AHP-DS hierarchical analysis to establish a recursive hierarchy for the network selection problem and obtains the subjective weights of network attributes through the judgment matrix. Then, it uses a genetic algorithm to adjust the subjective weight, defines the fitness function in the genetic algorithm-based on gray correlation analysis, adjusts the weights of the selection operator, crossover operator, and variation operator in the genetic algorithm, and gets the network with the largest fitness as the target network, which can effectively improve the user service quality.

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Section: Current Status Of Researchmentioning

confidence: 99%

[Retracted] Load Balancing Selection Method and Simulation in Network Communication Based on AHP‐DS Heterogeneous Network Selection Algorithm

Xiao

2021

Complexity

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“…Using a Markov model, the network selection is usually formulated as a joint user association and mobile traffic offloading problem. The target is to obtain a desirable admission and offloading policy to optimize certain systemlevel metrics such as service blocking probability [85]- [87].…”

Section: A Conventional User Association Approachesmentioning

confidence: 99%

AI-Assisted Network-Slicing Based Next-Generation Wireless Networks

Shen

Gao

et al. 2020

IEEE Open J. Veh. Technol.

265

115

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“…H ETEROGENOUS network convergence is a popular characteristic of fifth-generation (5G) mobileconnectivity networks [1][2][3]. It facilitates the meeting of diverse user needs pertaining to information communication and bandwidth allocation.…”

Section: Introductionmentioning

confidence: 99%

QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous Networks

2021

IEEE Access

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In the fifth-generation (5G) wireless-network system, the convergence of multiple networks of different standards as well as that of high-and low-frequency networks exists since a long time. Owing to the inability of 5G networks to predict the user quality of service (QoS) accurately, these networks are prone to issues such as access congestion, low QoS, and frequent congestion in one network while other network resources remain idle. Therefore, 5G networks fail to meet the QoS requirements and also prevent effective resource utilization. The deployment of multi-connectivity technologies can facilitate the optimization of the multinetwork convergence-system architecture. However, such technologies are faced with several challenges. Existing literature mainly focuses on the development of a multi-connectivity flowcontrol scheme to determine the secondary nodes (SNs) to which the master node (MN) should distribute data. This paper presents a three-step, QoS-forecasting, intelligent flow-control scheme, wherein the user equipment (UE) determines the data-flow direction based on the network characteristics and load handled by each node. Subsequently, the MN determines the initial user priority based on load balancing, user characteristics, and fairness. Finally, the MN allocates data to each SN in accordance with the QoS and average transmission capability of UE. The simulation results reveal that the proposed algorithm improves the system throughput significantly compared to the single connectivity and traditional fixed-data-split methods. Furthermore, the proposed method facilitates transmission-delay reduction, radio-link failureprobability control, and improved system robustness.

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Optimal Radio Access Technology Selection Algorithm for LTE-WiFi Network

Cited by 36 publications

References 30 publications

[Retracted] Load Balancing Selection Method and Simulation in Network Communication Based on AHP‐DS Heterogeneous Network Selection Algorithm

[Retracted] Load Balancing Selection Method and Simulation in Network Communication Based on AHP‐DS Heterogeneous Network Selection Algorithm

AI-Assisted Network-Slicing Based Next-Generation Wireless Networks

QoS-Forecasting-Based Intelligent Flow-Control Scheme for Multi-Connectivity in 5G Heterogeneous Networks

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