An adaptive context-aware vertical handover decision algorithm for heterogeneous networks

Satapathy, Pratyashi; Mahapatro, Judhistir

doi:10.1016/j.comcom.2023.06.029

Cited by 2 publications

(2 citation statements)

References 39 publications

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“…Te authors of [37] presented a context-aware algorithm adaptable to network specifcations and needs variations. Tey presented a channel allocation method that considers handover call trafc frst.…”

Section: Satapathy and Mahapatromentioning

confidence: 99%

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User Preference-Based Heterogeneous Network Management System for Vertical Handover

Ndegwa,

Nyachionjeka,

Mharakurwa

2023

Journal of Electrical and Computer Engineering

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Vertical handover management plays an essential role in wireless network technologies, mainly due to the rapid development of various radio access technologies (RATs) that require users to connect seamlessly from one RAT to another. However, in multiple RAT environments, vertical handover management encounters different challenges, including unnecessary handovers, handover failures, ping-pong handovers, and unsuitable access network selection. Essential in vertical handover management is maintaining the desired quality of service (QoS) by the mobile device user. The seamless movement of mobile device users as they run various applications depends on a well-performing vertical handover decision-making algorithm. This bears special significance in a heterogeneous network environment. This paper proposes a vertical handover algorithm that considers user preferences (i.e., a vertical handover algorithm that evaluates the application currently running on a user device). The main objective of the algorithm is to determine when it is necessary to perform the handover, depending on the applications running on the mobile device. The proposed algorithm utilizes a fuzzy logic system to assess whether the handover is necessary and a multiattribute decision-making (MADM) method to select the best available radio access network. A simulation scenario involving different applications at various mobile device velocities was developed. The results proved the algorithm’s effectiveness compared to some of the earlier proposed vertical handover algorithms. At velocities below 10 m/s and 30 m/s, the proposed algorithm had 0% and 15.02% unnecessary handovers, respectively, while the technique for order preference by similarity to ideal solution (TOPSIS) utility’s function-based algorithm obtained 12.38% and 23.24% at the same velocities, respectively. In addition, compared to TOPSIS, the obtained results of the proposed algorithm demonstrated a lower handover failure rate and ping-pong rate for a velocity span of 1–30 m/s for the considered user applications.

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Section: Satapathy and Mahapatromentioning

confidence: 99%

“…In this case, user preference and the network parameters, which include RSS, delay, data rate, BER, security, mobility, and cost, are used in the network ranking decision problem. Te network ranked the best in meeting user application needs is selected for vertical handover [35,37,49].…”

Section: Network Rankingmentioning

confidence: 99%

User Preference-Based Heterogeneous Network Management System for Vertical Handover

Ndegwa,

Nyachionjeka,

Mharakurwa

2023

Journal of Electrical and Computer Engineering

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Research of 5G HUDN network selection algorithm based on Dueling-DDQN

Xie,

Zhu,

2023

J Wireless Com Network

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Due to the dense deployment and the diversity of user service types in the 5G HUDN environment, a more flexible network selection algorithm is required to reduce the network blocking rate and improve the user’s quality of service (QoS). Considering the QoS requirements and preferences of the users, a network selection algorithm based on Dueling-DDQN is proposed by using deep reinforcement learning. Firstly, the state, action space and reward function of the user-selected network are designed. Then, by calculating the network selection benefits for different types of services initiated by users, the analytic hierarchy process is used to establish the weight relationship between the different user services and the network attributes. Finally, a deep Q neural network is used to solve and optimize the proposed target and obtain the user’s best network selection strategy and long-term network selection benefits. The simulation results show that compared with other algorithms, the proposed algorithm can effectively reduce the network blocking rate while reducing the switching times.

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An adaptive context-aware vertical handover decision algorithm for heterogeneous networks

Cited by 2 publications

References 39 publications

User Preference-Based Heterogeneous Network Management System for Vertical Handover

User Preference-Based Heterogeneous Network Management System for Vertical Handover

Research of 5G HUDN network selection algorithm based on Dueling-DDQN

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