An intelligent spectrum handoff scheme based on multiple attribute decision making for LTE-A network

Alhammadi, Abdulraqeb; Roslee, Mardeni; Alias, Mohamad Yusoff; Sheikhidris, Khalid; Jack, Yong Jun; Abas, A.; Randhava, Keshvinder Singh

doi:10.11591/ijece.v9i6.pp5330-5339

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…The author Alhammadi, et al [13] puts forwards "An intelligent spectrum handoff scheme based on multiple attribute decision making for LTE-A network." While the Ekti, et al [14] elaborates the utilization of the "Fuzzy Logic Approach for Layered Architecture Cognitive Radio Systems."…”

Section: Related Workmentioning

confidence: 99%

Performance Enhancements of Cognitive Radio Networks Using the Improved Fuzzy Logic

Darney¹,

Jacob²

2019

JSCP

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The rapid increase in the mobile device and the different types of wireless communication has led to the necessity of the extra spectrum allocation for the proper transmission of the information. Since the additional spectrum allocation for every network involved in the data transmission is a strenuous process, the efficient management of the spectrum allocation is preferred. The cognitive radio technology does a befitting service in the managing the allocation of the spectrum efficiently by providing the vacant spaces of the licensed users to the secondary users and vacating the secondary users when the licensed user request for the spectrum. This results in the deterioration in the performance of the secondary users due to the immediate evacuating. The conventional methods in the deciding the channel switching remains unsuitable for the cognitive radio network, so to have an effective decision on switching and selecting the channel the paper put forth the improved fuzzy logic that relies on the decision (IFDSS-GA) support system to handle both the switching of the channels and genetic algorithm to select the proper spectrum for conveyance. The evaluation of the proposed approach using the network simulator -2 determines the competency the IFDSS in terms of the throughput and switching rate.

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Section: Related Workmentioning

confidence: 99%

Performance Enhancements of Cognitive Radio Networks Using the Improved Fuzzy Logic

Darney¹,

Jacob²

2019

JSCP

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“…The system comprises of a fuzzy logic controller (FLC) with inputs such as eNB load, received power, arrival rate of primary users. The suggested method offers a strong framework for preventing unwanted CRN handoffs in LTE-A systems [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

GRU-SVM Based Threat Detection in Cognitive Radio Network

Ezhilarasi

Clement

2023

Sensors

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Cognitive radio networks are vulnerable to numerous threats during spectrum sensing. Different approaches can be used to lessen these attacks as the malicious users degrade the performance of the network. The cutting-edge technologies of machine learning and deep learning step into cognitive radio networks (CRN) to detect network problems. Several studies have been conducted utilising various deep learning and machine learning methods. However, only a small number of analyses have used gated recurrent units (GRU), and that too in software defined networks, but these are seldom used in CRN. In this paper, we used GRU in CRN to train and test the dataset of spectrum sensing results. One of the deep learning models with less complexity and more effectiveness for small datasets is GRU, the lightest variant of the LSTM. The support vector machine (SVM) classifier is employed in this study’s output layer to distinguish between authorised users and malicious users in cognitive radio network. The novelty of this paper is the application of combined models of GRU and SVM in cognitive radio networks. A high testing accuracy of 82.45%, training accuracy of 80.99% and detection probability of 1 is achieved at 65 epochs in this proposed work.

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“…Due to the change in topology, the data transmission using DSRC depends on routing [14]. The routes are selected using Q-learning, sweep algorithm and deep reinforcement learning algorithm [15]- [18]. In [14] the authors have proposed a cluster based handoff and proposed dynamic edge-backup node (DEBCK).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence based handover decision and network selection in heterogeneous internet of vehicles

Hussain

Yusof

Asuncion

et al. 2021

IJEECS

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Internet of vehicles (IoV) is an emerging area that gives support for vehicles via internet assisted communication. IoV with 5G provides ubiquitous connectivity due to the participation of more than one radio access network. The mobility of vehicles demands to make handover in such heterogeneous network. The vehicles at short range uses dedicated short range communication (DSRC), while it has to use better technology for long range and any type of traffic. Usually, the previous work will directly select the network for handover or it connects with available radio access. Due to this, the occurrence of handover takes place frequently. In this paper, the integration of DSRC, LTE and mmWave 5G on IoV is incorporated with novel handover decision making, network selection and routing. The handover decision is to ensure whether there is a need for vertical handover by using Dynamic Q-learning algorithm that uses entropy function for threshold prediction as per the current characteristics of the environment. Then the network selection is based on fuzzy-convolution neural network (F-CNN) that creates fuzzy rules from signal strength, distance, vehicle density, data type and line of sight. V2V chain routing is proposed to select V2V pairs using jellyfish optimization algorithm (JOA) that takes in account of channel, vehicle and transmission metrics. This system is developed in OMNeT++ simulator and the performances are evaluated in terms of success probability, handover failure, unnecessary handover, mean throughput, delay and packet loss.

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An intelligent spectrum handoff scheme based on multiple attribute decision making for LTE-A network

Cited by 4 publications

References 11 publications

Performance Enhancements of Cognitive Radio Networks Using the Improved Fuzzy Logic

Performance Enhancements of Cognitive Radio Networks Using the Improved Fuzzy Logic

GRU-SVM Based Threat Detection in Cognitive Radio Network

Artificial intelligence based handover decision and network selection in heterogeneous internet of vehicles

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