Optimization of user behavior based handover using fuzzy Q-learning for LTE networks

Hegazy, Rana D.; Nasr, Omar A.; Kamal, Hanan A.

doi:10.1007/s11276-016-1348-2

Cited by 24 publications

(26 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Online English learning also overemphasizes the subjectivity of tools but neglects the assistance of tools. In view of this situation and the essence of multiagent in English online learning, the main solution in the current industry is to make full use of the multiagent system to improve the dictionary system [10].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Optimization of English Online Learning Dictionary System Based on Multiagent Architecture

Wang

2021

Complexity

View full text Add to dashboard Cite

As a universal language in the world, English has become a necessary language communication tool under the globalization of trade. Intelligent, efficient, and reasonable English language-assisted learning system helps to further improve the English ability of language learners. English online learning dictionary, as an important query tool for English learners, is an important part of English online learning. This paper will optimize the design of English online learning dictionary system based on multiagent architecture. Based on the hybrid multiagent cooperative algorithm, this paper will improve the disadvantages of the online English learning dictionary system and propose an appropriate dictionary application evaluation function. At the same time, an improved reinforcement learning algorithm is introduced into the corresponding English online learning dictionary navigation problem so as to improve the efficiency of the online English learning dictionary system. English online learning dictionary is more intelligent and efficient. In this paper, the new online learning dictionary system optimization algorithm is proposed and compared with the traditional system algorithm. The experimental results show that the algorithm proposed in this paper solves the collaborative confusion problem of English learning online dictionary to a certain extent and further solves the corresponding navigation problem so as to improve the efficiency.

show abstract

Section: Introductionmentioning

confidence: 99%

[Retracted] Optimization of English Online Learning Dictionary System Based on Multiagent Architecture

Wang

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…By the work, the throughput rate has been increased by maximizing the data rate in the macro cells. Further, in [26], a fuzzy Q-learning based handover optimization model has been derived. And, the optimizations were done with addressing two issues, ping pongs and failures of radio links.…”

Section: Related Workmentioning

confidence: 99%

Elite Opposition Based Metaheuristic Framework for Load Balancing in LTE Network

Sivagar¹,

Prabakaran²

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

In present scenario of wireless communications, Long Term Evolution (LTE) based network technology is evolved and provides consistent data delivery with high speed and minimal delay through mobile devices. The traffic management and effective utilization of network resources are the key factors of LTE models. Moreover, there are some major issues in LTE that are to be considered are effective load scheduling and traffic management. Through LTE is a depraved technology, it is been suffering from these issues. On addressing that, this paper develops an Elite Opposition based Spider Monkey Optimization Framework for Efficient Load Balancing (SMO-ELB). In this model, load computation of each mobile node is done with Bounding Theory based Load derivations and optimal cell selection for seamless communication is processed with Spider Monkey Optimization Algorithm. The simulation results show that the proposed model provides better results than exiting works in terms of efficiency, packet delivery ratio, Call Dropping Ratio (CDR) and Call Blocking Ratio (CBR).

show abstract

“…Since it does not exploit contextual information, such as movement velocity and application requirements for enhancing HM, SSF can lead to unnecessary and frequent handovers, decreasing throughput, increasing packet loss and even causing network service disruption. In [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ] handover decisions are made by using multicriteria and techniques such as Fuzzy Logic, Network Management Policies or Multiple Attribute Decision Making (MADM) Algorithms. Nonetheless, these approaches neglect an information model that disregards criteria from one or more information domains, such as network characteristics and status, application requirements, end-user profile, end-user device features, or handover history, which are relevant for advancing HM.…”

Section: Related Workmentioning

confidence: 99%

“…These handover issues can decrease throughput, increase packet loss and even cause network service disruption [ 10 , 11 , 12 ]. The multicriteria-based approaches in [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ] use RSSI and context information as criteria for ranking the available networks; the top-ranked network is selected by the end-user device for performing the connection process. These approaches disregard one or more relevant criteria, such as wireless network characteristics (e.g., coverage area), user device features (e.g., battery consumption), application requirements (e.g., real-time response) or user peculiarities (e.g., mobility pattern), leading to the failure of handovers and wrong network selection, negatively impacting the network’s performance [ 12 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

A Semantic and Knowledge-Based Approach for Handover Management

Vivas

Rendón

Nieves

2021

Sensors

View full text Add to dashboard Cite

Handover Management (HM) is pivotal for providing service continuity, enormous reliability and extreme-low latency, and meeting sky-high data rates, in wireless communications. Current HM approaches based on a single criterion may lead to unnecessary and frequent handovers due to a partial network view that is constrained to information about link quality. In turn, HM approaches based on multicriteria may present a failure of handovers and wrong network selection, decreasing the throughput and increasing the packet loss in the network. This paper proposes SIM-Know, an approach for improving HM. SIM-Know improves HM by including a Semantic Information Model (SIM) that enables context-aware and multicriteria handover decisions. SIM-Know also introduces a SIM-based distributed Knowledge Base Profile (KBP) that provides local and global intelligence to make contextual and proactive handover decisions. We evaluated SIM-Know in an emulated wireless network. When the end-user device moves at low and moderate speeds, the results show that our approach outperforms the Signal Strong First (SSF, single criterion approach) and behaves similarly to the Analytic Hierarchy Process combined with the Technique for Order Preferences by Similarity to the Ideal Solution (AHP-TOPSIS, multicriteria approach) regarding the number of handovers and the number of throughput drops. SSF outperforms SIM-Know and AHP-TOPSIS regarding the handover latency metric because SSF runs a straightforward process for making handover decisions. At high speeds, SIM-Know outperforms SSF and AHP-TOPSIS regarding the number of handovers and the number of throughput drops and, further, improves the throughput, delay, jitter, and packet loss in the network. Considering the obtained results, we conclude that SIM-Know is a practical and attractive solution for cognitive HM.

show abstract

Optimization of user behavior based handover using fuzzy Q-learning for LTE networks

Cited by 24 publications

References 24 publications

[Retracted] Optimization of English Online Learning Dictionary System Based on Multiagent Architecture

[Retracted] Optimization of English Online Learning Dictionary System Based on Multiagent Architecture

Elite Opposition Based Metaheuristic Framework for Load Balancing in LTE Network

A Semantic and Knowledge-Based Approach for Handover Management

Contact Info

Product

Resources

About