A heterogeneous network selection algorithm based on network attribute and user preference

Huang, Yu; Zhang, Biao

doi:10.1016/j.adhoc.2018.01.011

Cited by 67 publications

(51 citation statements)

References 20 publications

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“…To avoid human interference, an objective weighting methodentropy is utilised to calculate weight value of each criterion as shown in Eqs. (22) and (23). The entropy is originally derived from the concepts of thermodynamics and it measures the amount of instability in a system.…”

Section: Initialisation-generate Decision Matrix and Weight Arraymentioning

confidence: 99%

“…Then, the weight value of each criterion can be calculated by Eqs. (22) and (23), as in Table 1, The normalised decision matrix and weight array will be mapped into membership functions and then transformed into fuzzy triangular membership number as in Table 2. To simplify, the generalised fuzzy membership functions (see Fig.2) are used in the numerical example.…”

Section: Numerical Analysismentioning

confidence: 99%

“…TOPSIS is favoured due to its robustness and reliability, as discussed in surveys [14]- [16]. The TOPSIS approach was first developed by Hwang and Yoon [17] and is widely used for target BS selection, as reported in [18]- [22]. References [18] and [19] proposed a MADM based handover scheme to eliminate unnecessary handover in LTE-Advance and UDN.…”

Section: Introductionmentioning

confidence: 99%

“…These two weighting approaches can effectively avoid errors caused by human subjective decisions. Research works in [21] and [22] demonstrated an enhanced TOPSIS based handover scheme by combining TOPSIS with other MADM schemes, i.e. utility function and analytical hierarchy process.…”

Section: Introductionmentioning

confidence: 99%

“…Simulation results in [18]- [22] shows that TOPSIS approach can effectivity deal with multiple handover related criteria and select an optimal handover target, which can significantly reduce ping-pong effect, number of handover failures, and enhancing user throughput. However, conventional MADM approach cannot deal directly with any form of imprecise data [23].…”

Section: Introductionmentioning

confidence: 99%

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A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks

et al. 2019

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As the global data traffic has significantly increased in the recent year, the ultra-dense deployment of cellular networks (UDN) is being proposed as one of the key technologies in the fifthgeneration mobile communications system (5G) to provide a much higher density of radio resource. The densification of small base stations (BSs) could introduce much higher inter-cell interference and lead user to meet the edge of coverage more frequently. As the current handover scheme was originally proposed for macro BS, it could cause serious handover issues in UDN i.e. ping-pong handover, handover failures and frequent handover. In order to address these handover challenges and provide a high quality of service (QoS) to the user in UDN. This paper proposed a novel handover scheme, which integrates both advantages of fuzzy logic and multiple attributes decision algorithms (MADM) to ensure handover process be triggered at the right time and connection be switched to the optimal neighbouring BS. To further enhance the performance of the proposed scheme, this paper also adopts the subtractive clustering technique by using historical data to define the optimal membership functions within the fuzzy system.Performance results show that the proposed handover scheme outperforms traditional approaches and can significantly minimise the number of handovers and the ping-pong handover while maintaining QoS at a relatively high level.

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Section: Initialisation-generate Decision Matrix and Weight Arraymentioning

confidence: 99%

Section: Numerical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks

et al. 2019

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Reinforcement learning and neuro‐fuzzy GNN‐based vertical handover decision on internet of vehicles

Kumar

Sagar

2023

Concurrency and Computation

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The Internet of Vehicles (IoV) has become a lot of concern for scientific society because of its rising importance in creating an intelligent transportation system. However, the existing systems addressed handover and network selection difficulties, but they did not solve the necessity for frequent handover problems.Hence, in this research, handover decision difficulties are removed by using a novel state-action-reward-state-action reinforcement learning method in which it learns the current policy action to determine whether a handover decision is required in the fast-moving vehicle therefore the handover failure problem is reduced. Moreover, the existing fuzzy rules do not consider non-line-of-sight and the learning capabilities of the neural networks to select the suitable network therefore it creates network selection difficulty. Hence, the network selection difficulties are removed by using a novel fuzzy-based graph neural network in which the fuzzy logic is connected to the graph hierarchical structure to select the right network therefore the efficient and suitable network is selected on the internet of vehicle communication. Moreover, the existing process is to select the routes completely established on the ancient broadcast history of the vehicles and their channel metrics which causes the frequent handover and poor performance of the network. Hence, the routing selection problem is eliminated by using a novel cluster-based routing algorithm in which the vehicle was divided into clusters to select the efficient route of the internet of the vehicle therefore the best routing path is carried out. As a result, the proposed system is improving the quality of the service and reducing the handover failure and delay of the system.

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Reducing unnecessary handovers and improving ranking abnormality based on multi‐attribute decision making graph theory and matrix approach with Euclidean distance in heterogeneous wireless networks

Kaur

Goyal

Mehta

2023

Concurrency and Computation

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SummaryNew mobile devices offer multiple network interfaces to allow the users to connect to the best available network. The heterogeneous networks can provide better internet connectivity to the users by means of vertical handover. The handover must be triggered at a suitable point of time to avoid mobility issues such as unnecessary handovers and handover ping‐pongs. The network selection during handover is usually done using classical multi‐attribute decision making (MADM) methods. However, ranking abnormality is one of the prominent issues of the classical MADM methods. To address these challenges, a graph theory and matrix approach (GTMA) with Euclidean distance is proposed for vertical handover in wireless networks. GTMA is used for the selection of the appropriate network and Euclidean distance is utilized for the handover triggering. The simulation results reveal that the proposed method has eliminated the ranking abnormality issue. This proposed technique without triggering has also reduced the number of handovers up to 75.61%, 85.71%, and 66.67% as compared to the traditional MADM methods such as AHP, GRA, and TOPSIS respectively. The use of Euclidean distance for handover triggering has further reduced the number of handovers of the proposed technique as well as traditional techniques for all the traffic types.

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A heterogeneous network selection algorithm based on network attribute and user preference

Cited by 67 publications

References 20 publications

A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks

A fuzzy-clustering based approach for MADM handover in 5G ultra-dense networks

Reinforcement learning and neuro‐fuzzy GNN‐based vertical handover decision on internet of vehicles

Reducing unnecessary handovers and improving ranking abnormality based on multi‐attribute decision making graph theory and matrix approach with Euclidean distance in heterogeneous wireless networks

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