network selection
Recently Published Documents

The problem of selecting a wireless access network in a highly heterogeneous environment has been analyzed and solved. A network selection model based on the analysis of a wireless network environment using a federated reinforcement machine learning system is proposed. A model has been developed to estimate the theoretical average capacity available to the user in a highly heterogenic access network. The effectiveness of the proposed method was evaluated using a series of experiments. The article is concluded with a discussion regarding the applicability of the proposed method for IMT-2020 and IMT-2030 networks.

The selection of road networks is very important for cartographic generalization. Traditional artificial intelligence methods have improved selection efficiency but cannot fully extract the spatial features of road networks. However, current selection methods, which are based on the theory of graphs or strokes, have low automaticity and are highly subjective. Graph convolutional networks (GCNs) combine graph theory with neural networks; thus, they can not only extract spatial information but also realize automatic selection. Therefore, in this study, we adopted GCNs for automatic road network selection and transformed the process into one of node classification. In addition, to solve the problem of gradient vanishing in GCNs, we compared and analyzed the results of various GCNs (GraphSAGE and graph attention networks [GAT]) by selecting small-scale road networks under different deep architectures (JK-Nets, ResNet, and DenseNet). Our results indicate that GAT provides better selection of road networks than other models. Additionally, the three abovementioned deep architectures can effectively improve the selection effect of models; JK-Nets demonstrated more improvement with higher accuracy (88.12%) than other methods. Thus, our study shows that GCN is an appropriate tool for road network selection; its application in cartography must be further explored.

Nowadays, mobile users are equipped with multi-mode terminals allowing them to connect to different radio access technologies like WLAN, 3G (HSPA and HSPA+), and Long term evolution (LTE) each at a time. In this context, the challenge of the next-generation networks is to achieve the Always Best Connected (ABC) concept. To this end, solving the problem of selecting the most suitable radio access technology (RAT) from the list of available RAT is at the heart of the next-generation systems. The decision process is called access network selection and it depends on several parameters, such as quality of service, mobility, cost of each RAT, energy consumption, battery life, etc. Several methods and approaches have been proposed to solve the network selection problem with the fundamental objective which is to offer the best QoS to the users and to maximize the usability of the networks without affecting the users’ experience. In this paper, we propose an adaptive KNN (K nearest neighbour) based algorithm to solve the network selection problem, the proposed solution has a low computation complexity with a high level of veracity is compared with the well-known MADM methods.

Future wireless communication networks will be composed of different technologies with complementary characteristics. Thus, vertical handover (VHO) must support seamless mobility in such heterogeneous environments. The network selection is an important phase in the VHO process and it can be formulated as a multiattribute decision-making problem. So, the mobile terminal equipped with multiple interfaces will be able to choose the most suitable network. This work proposes an access network selection algorithm, based on cosine similarity distance, subjective weights using Fuzzy ANP, and objective weights using particle swarm optimization. The comprehensive weights are based on the cosine similarity distance between the networks and the ideal network. Finally, the candidate network with the minimum cosine distance to the ideal network will be selected in the VHO network selection stage. The performance analysis shows that our proposed method, based on cosine similarity distance and combination weights, reduces the ranking abnormality and number of handoffs in comparison with other MADM methods in the literature.

Artificial Neural Networks (ANN) can be designed as a nonparametric tool for time series modeling. MATLAB serves as a powerful environment for ANN modeling. Although Neural Network Time Series Tool (ntstool) is useful for modeling time series, more detailed functions could be more useful in order to get more detailed and comprehensive analysis results. For these purposes, cbnet function with properties such as input lag generator, step-ahead forecaster, trial-error based network selection strategy, alternative network selection with various performance measure and global repetition feature to obtain more alternative network has been developed, and MATLAB algorithms and source codes has been introduced. A detailed comparison with the ntstool is carried out, showing that the cbnet function covers the shortcomings of ntstool.