Network selection algorithm for heterogeneous wireless networks based on service characteristics and user preferences

Liang, Gen; Huang, Yu

doi:10.1186/s13638-018-1264-5

Cited by 34 publications

(17 citation statements)

References 36 publications

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“…Liang and Yu [ 32 ] divided user services into different types and calculated the utility value of each network attribute by using utility functions according to the characteristics of different services. Then, the entropy method and the FAHP are used to calculate the objective and subjective weight of network attributes, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Then, a fuzzy consistent matrix is constructed using these ratios. According to literature [ 32 ], the fuzzy consistent matrices for voice service, video service, and data service are, respectively, shown (Tables 2 – 4 ), and the consistency of these matrices is checked according to equation ( 6 ). Finally, the weight of each judgment parameter is calculated according to equation ( 7 ).…”

Section: System Modelmentioning

confidence: 99%

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A User-Oriented Intelligent Access Selection Algorithm in Heterogeneous Wireless Networks

Liang

Guo

Fang

2020

Computational Intelligence and Neuroscience

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A heterogeneous wireless network (HWN) contains many kinds of wireless networks with overlapping areas of signal coverage. One of the research topics on HWNs is how to make users choose the most suitable network. This paper designs a user-oriented intelligent access selection algorithm in HWNs with five modules (input, user preference calculation, candidate network score calculation, output, and learning). Essentially, the input module uses a utility function to calculate the utility value of the judgment parameter; the user preference calculation module calculates the weight of the judgment parameter using the fuzzy analysis hierarchy process (FAHP) approach; the candidate network score calculation module calculates the network score through a fuzzy neural network; the output module calculates the error between the actual output value and the expected output value; and the learning module corrects the parameter of the membership function in the fuzzy neural network structure according to the error. Simulation results show that the algorithm proposed in this paper can enable users to select the most suitable network according to service characteristics and can enable users to obtain higher gains.

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Section: Related Workmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

A User-Oriented Intelligent Access Selection Algorithm in Heterogeneous Wireless Networks

Liang

Guo

Fang

2020

Computational Intelligence and Neuroscience

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“…In [28], Liang et al designed an access selection algorithm combining service characteristics and user preferences. First, the algorithm calculates the utility value of each network attribute for different applications using the utility functions.…”

Section: Related Workmentioning

confidence: 99%

Multi-Attribute Access Selection Algorithm for Heterogeneous Wireless Networks Based On Uncertain Network Attribute Values

Guo

Omar

Zaini

et al. 2020

Preprint

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A heterogeneous wireless network (HWN) environment contains many kinds of wireless networks, such as UMTS, LTE, and WLAN, where users move around within their coverage area. How to ensure mobile users select the most suitable network is a hot research topic for HWNs. While traditional access selection algorithms assume that mobile users can obtain accurate network attribute values, the network attribute values obtained by mobile users are often uncertain due to the mobility of users, the interference of wireless signals, and the fluctuation of the network state. To solve this problem, this paper designs an access selection algorithm for HWNs in the context of inaccurate network attribute values. First, the algorithm calculates the network attribute values based on the hesitant fuzzy theory, then calculates the weights of network attributes using the fuzzy analytic hierarchy process (FAHP), and finally sorts the candidate networks using the hesitant fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. The simulation results show the proposed algorithm enables users to select the most suitable network to access under the inaccurate network attribute environment and obtain higher gains.

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“…Despite significant work done on QoS provisioning [9], [10], existing protocol of resource scheduling design [11]- [14] predominantly considering improving just throughput and resource utilization under homogenous network; however, improving user quality of experience play very important role in future generation cellular network which is heterogeneous in nature [15]- [17]. Recently, deep learning and reinforcement learning approach have been adopted for resource provisioning in cellular network [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

Delay aware downlink resource allocation scheme for future generation tactical wireless networks

Shankar

Patil

2021

IJ-AI

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<span lang="EN-US">For a very long time protecting physical border integrity is considered to be a challenging thing. Government organizations must provide trade operations for economic growth and at the same time must prevent malicious activity. A different resource such as drones, sensors, and radars are used for monitoring border areas which must be communicated to the remote border security force. Efficient wireless communication is required for communicating information. However, these devices cannot connect to a centralized network directly; thus, are connected in an ad-hoc fashion to connect centralized server. Different tactical network applications require different quality of service (QoS); hus efficient resource scheduling plays a very important role. Existing resource scheduling adopting deep learning and reinforcement techniques fails to meet the quality of experience (QoE) of the user and doesn’t assure access fairness among contending users. Further, require network information in prior and induce high training time. For overcoming research issues, this paper presents a delay-aware downlink resource scheduling (DADRA) technique for future generation networks. The optimization problem of reducing buffer overflow and improving scheduling QoS performance is solved using a genetic algorithm with an improved crossover function. Experiment outcome shows DADRA achieves much better throughput, slot utilization, and packet failure performance when compared with standard resource allocation technique.</span>

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Network selection algorithm for heterogeneous wireless networks based on service characteristics and user preferences

Cited by 34 publications

References 36 publications

A User-Oriented Intelligent Access Selection Algorithm in Heterogeneous Wireless Networks

A User-Oriented Intelligent Access Selection Algorithm in Heterogeneous Wireless Networks

Multi-Attribute Access Selection Algorithm for Heterogeneous Wireless Networks Based On Uncertain Network Attribute Values

Delay aware downlink resource allocation scheme for future generation tactical wireless networks

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