Abstract
Interdependency among system parameters may significantly affect the cross-layer design and optimization performance of wireless networks. However, it is very difficult to derive the interdependency among system parameters in a dynamic complex networking system due to the uncertainty of data observation and system modeling. In this research, we propose a new approach for dynamic interdependency measure among system parameters by using non-additive measure theory. In particular, the Choquet integral model is applied to distinguish the interaction among system parameters towards the objective function through a set of non-additive measures. Then the most significant effect subset of system parameters on the performance metrics of interest for system objective function is identified. The simulations show that it is only need to adjust the parameters in the optimized subset according to the interdependency measure can improved the network throughput, consequently, the radio resource is utilized more reasonably.
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