Heterogeneous Wireless Network Selection Based On Multi-Attribute Decision Making

The development of wireless technology has brought a revolutionary change to the communications industry. A variety of technologies provide users with high-quality broadband access and seamless connectivity services. The next generation network systems will has different access network technologies, which have different bandwidth, latency, response time, safety, and cost factor. These multi-attributes can not only be directly compared, but cannot be substituted. So it is inevitable that these wireless heterogeneous networks will be integrated in the future.In the wireless heterogeneous environment, mobile users can access to a wireless network and switch to another freely in future. As well as based on user policy (network conditions or user preference), users can choose the most appropriate network access at the right time. Based on the results of previous studies, our study is based on multi-attribute decision model considering different users have different preferences and different network services need different QoS and transmission rate, and then build the heterogeneous wireless network selection model, which can solve the questions:"connection which network" and "what the network selection criterion is".The model in this paper consists of two layers. The first layer of model is called the multi-attribute fuzzy subsystem. The second layer is called the multi-attribute decision making (MCDM) systems.In the first layer of the multi-attribute fuzzy subsystem, this study suggests that the relationship between effectiveness of the network and user is not simple monotonically increasing or decreasing. According to the fact different services match different network QoS and transmission rate, our study will use of the fuzzy math to set functions of each network attribute which can calculate users’ utility value.In the second layer of multi-attribute decision-making system, the decision function is based on a linear weighting function, taking into account the type of services and users’ preferences on price or quality by introducing new definitions which called the network elimination factor and the new price function. In addition, before triggering the network switching algorithm, there is a "lazy time".In order to minimize the network loading by users’ frequently switches between the networks, thus making the network resources be more effectively utilized.Finally, this study demonstrates the effectiveness of the algorithm by comparing the original multi-attribute decision algorithm with the algorithm we build.