Jiangtao Dong

Location Prediction Model Based on Bayesian Network Theory

Location prediction is one of the key technologies of active mobility management in the next generation of mobile communication systems. Most of known location prediction models only take parts of predictive factors into account, which leads to a low prediction success ratio of these models. The motivation of this paper is to design a location prediction model considering multiple predictive factors to improve the prediction success ratio and improve the efficiency of the model. In this paper, a location prediction model based on Bayesian Network theory is proposed. The proposed model can effectively solve multi-factor location prediction. Firstly, the relative predictive factors are coded in the Bayesian Network node, and location prediction results can be calculated based on cell topology information integrated in the model structure. A factors distribution mechanism is designed to solve the problem when the nodes cannot obtain location prediction information directly. Subsequently, the method of calculating location prediction results for each cell is presented. The simulation results indicate that the proposed location prediction model is effective in improving accuracy of location prediction and the stability of the model is better than that in comparative schemes.

A Weighted Bipartite Graph Based Network Selection Scheme for Multi-Flows in Heterogeneous Wireless Network

Network selection is an important issue in the next generation of heterogeneous wireless network and well studied for individual flows. However, researches of network selection for multi-flows at network side are seldom touched upon but also important from the global view of optimizing usage of network resources. In this paper, a weighted bipartite graph based network selection scheme for multi-flows is proposed, which adopt secondary exponential smoothing method to perform network resource prediction and assign flows to networks based on Matching Degree(MD). The network selection scheme is modeled as weighted bipartite graph with objectives of maximizing Matching Degrees and access ratio as well as the constraint of guaranteeing no network overloaded. Weighted Bipartite Graph Algorithm (WBGA) is designed to achieve the goals. Simulation results show that WBGA demonstrates best performance compared to other schemes, with regard to average MD, access ratio and utilization ratio of network resources.

The Analysis of the Optimal Periodic Ranging Slot Number in IEEE 802.16 OFDMA Systems

As part of the IEEE 802.16 protocol, the random access scheme is used for ranging. The ratio between the numbers of periodic ranging slots and data slots in the uplink subframe decides the contention throughput, ranging delay and data throughput, all of which are important parameters of the system performance. However no specific ratio is standardized in the protocol. So, it is necessary to determine the optimal number of periodic ranging slots which is the decisive factor of the ratio. In this paper, the three parameters above are analyzed and the optimal number of the periodic ranging slots is derived. In addition, an estimation method is proposed to predict the number of contention users. The correctness of the analysis is verified by the simulation results, and it is demonstrated that the contention throughput efficiency is high with the derived optimal number.