Joint Access Selection and Bandwidth Allocation Algorithm Supporting User Requirements and Preferences in Heterogeneous Wireless Networks

Liang, Gen; Huang, Yu; Guo, Xiaoxue; Qin, Yong

doi:10.1109/access.2019.2899405

Cited by 27 publications

(9 citation statements)

References 37 publications

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“…To prove the superiority of the algorithm proposed in this paper, this algorithm is compared with three algorithms proposed in other papers (namely, the utility and FAHP algorithm proposed in literature [ 31 ], the AHP and TOPSIS algorithm proposed in literature [ 33 ], and the fuzzy logic and neural network algorithm proposed in literature [ 35 ]). These three algorithms are, respectively, called Algorithm 1, Algorithm 2, and Algorithm 3.…”

Section: Simulation and Results Analysismentioning

confidence: 99%

“…In HWNs where WiMAX, LTE, and WLAN coexist, Liang et al [ 35 ] used the RSS, network load, and user rate demand as decision parameters and calculated the score and bandwidth allocation value of each candidate network through a five-layer fuzzy neural network structure. While the algorithm can modify fuzzy rules according to users' preferences and adjust the resource utilization rate of different networks, the increase in the number of candidate networks and decision parameters may lead to a sharp increase in the fuzzy rule base and increase the time delay in access selection.…”

Section: Related Workmentioning

confidence: 99%

“…According to literature [ 35 ], the learning rules of the parameters c j (5) and σ j (5) of the Layer 5 membership function, respectively, are

…”

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: Simulation and Results Analysismentioning

confidence: 99%

Section: Related Workmentioning

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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“…In the active scan the mobile radio transmits a probe request message and then listens for the probe response message from the AP [15]. In the passive scan, in order for the wireless stations to get the APs information, they listen to the periodically transmitted beacon frames from the AP [16]. The load balancing in Wi-Fi can be classified into two methods, client-driven or centralized, depending upon which part of the network is in charge of AP association decisions [17].…”

Section: Related Work a Load Balancing In Wi-fimentioning

confidence: 99%

Towards QoS-Aware Load Balancing for High Density Software Defined Wi-Fi Networks

et al. 2020

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High density Wi-Fi networks require load balancing in order to ensure quality of service (QoS). In the traditional Wi-Fi networks, the wireless stations learn the access points (APs) load and make the association decisions themselves leading to uneven load distribution among the APs. The new paradigm, software defined networking (SDN), has a centralized architecture, which allows to manage, measure and control high density Wi-Fi networks easily. In this paper we propose a QoS-aware load balancing strategy (QALB) for software defined Wi-Fi networks (SD-Wi-Fi), as a solution to address the problem of Wi-Fi congestion among the OpenFlow enabled APs (OAPs). The SDN controller selects a load level up to which the association decisions are made by the OAPs without consulting the controller. The wireless stations from an overloaded OAP are handed to an underloaded OAP by considering multi-metrics such as the packet loss rate, received signal strength indicator (RSSI) and throughput. An emulation platform and a large-scalelow-cost testbed with the same settings are constructed to evaluate the performance of our load balancing strategy. The results show that in comparison to four non-static schemes such as, channel measurement based access selection scheme (CMAS), (DL-SINR) downlink-signal to interference plus noise ratio AP selection scheme (DASA), mean probe delay scheme (MPD) and RSSI scheme, the proposed QALB, optimizes the throughput up to 16%, reduces the average frame delay up to 19%, minimizes the number of re-transmissions by 49%, reduces the number of handoffs by 15%, improves the degree of load balancing by 22% and minimizes the re-association times by 38%, in high density SD-Wi-Fi. INDEX TERMS Emulation, high density, load balancing, QoS, Wi-Fi, SDN, testbed.

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“…Traditional access selection algorithms usually adopt mathematical models, such as multi-attribute decision making (MADM) [8,9], utility theory [10,11], fuzzy logic [12,13], game theory [14][15][16], optimal calculation [17,18], and neural network [19][20][21].…”

Section: Introductionmentioning

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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Joint Access Selection and Bandwidth Allocation Algorithm Supporting User Requirements and Preferences in Heterogeneous Wireless Networks

Cited by 27 publications

References 37 publications

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

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

Towards QoS-Aware Load Balancing for High Density Software Defined Wi-Fi Networks

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

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