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Research On The Algorithms For Handoff In Hierarchical Wireless Networks

Posted on:2016-08-18Degree:MasterType:Thesis
Country:ChinaCandidate:Y ZhouFull Text:PDF
GTID:2348330488471494Subject:Signal and Information Processing
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In recent years, various wireless networks such as cellular network, wireless local area network and wireless sensor network have developed rapidly. But these wireless networks have great differences in coverage, fee standards, bandwidth and so on. Realizing the integration of these wireless networks can fully utilize network resources, improve the capacity of the network, provide better service to users and reduce the cost of network infrastructure. For example, the cellular network has such advantages such as a wide coverage, uninterrupted network connection for users and data transmission with high reliability. But its biggest drawback is the low transmission rate. On the other hand, the wireless local area network can provide wireless broadband services and ensure users with the ability of high data transmission rate. So it makes up the defect of cellular network in data transmission. But it also has many disadvantages such as limited coverage and limited communication range. Thus it can be seen that these differences between cellular network and wireless local area network are complementary. So a combination of these different wireless networks can provide users with higher quality of service. Vertical handoff is the key technology of network convergence. So it is very necessary to learn and research the algorithm of the vertical handoff. This paper aims at the algorithms of vertical handoff in heterogeneous wireless networks and propose some appropriate solutions, the main work are as follows:Firstly, the paper describes the heterogeneous wireless networks from two aspects of the characteristics and technical problems. The mobility management, especially the technology of vertical handoff is deeply studied. After studying the factors that affect vertical handoff decision and the performance indicators of the algorithm, this paper introduces and analyses several typical algorithms of vertical handoff.Secondly, many attributes need to be taken into account in the algorithm of vertical handoff, but many current researchers only consider individual decision factors, some other researchers consider many factors but obtain the weight subjectively. To solve these problems, this paper proposes a vertical handoff algorithm which is based on fuzzy logic and adjusted weights. In this algorithm, it is regarded as a multi-attribute decision-making problem. It uses rough set theory to assure the core attributes of network selection, and utilizes fuzzy logic control to process data. Then it uses entropy method to adjust the weights which are obtained through analytic hierarchy process, and makes sure the final weights are the best to the objective optimization model. Finally, the paper takes simulation experiment, and the results show that this algorithm has a good performance in the number of handoffs and the access blocking probability, and it reflects that each terminal service can have its own handoff performance.Finally, this paper has a research of load balancing in heterogeneous wireless network and it named a vertical handoff algorithm based on dynamic load balancing. In this algorithm, it uses the fuzzy logic to process the property factors and introduces the neural network to keep heterogeneous wireless network load balance. In addition, it introduces preprocess model to handle some simple cases before the fuzzy neural network model. It can reduce some unnecessary calculations, and the simulation results shows that introducing the preprocess model can reduce the delay of handoff. Compared with the traditional algorithm, this algorithm can effectively decrease the handoff call blocking probability and the access blocking probability,which proves the effectiveness of this algorithm in the network dynamic load balancing.
Keywords/Search Tags:heterogeneous wireless networks, vertical handoff, fuzzy logic, dynamic load balancing, fuzzy neural network
PDF Full Text Request
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