Pasi Lehtimäki

Advanced analysis methods for 3G cellular networks

The operation and maintenance of the third generation (3G) mobile networks will be challenging. These networks will be strongly service driven, and this approach differs significantly from the traditional speech dominated in the second generation (2G) approach. Compared to 2G, in 3G, the mobile cells interact and interfere with each other more, they have hundreds of adjustable parameters, and they monitor and record data related to several hundreds of different variables in each cell. This paper shows that a neural network algorithm called the self-organizing map, together with a conventional clustering method like the k-means, can effectively be used to simplify and focus network analysis. It is shown that these algorithms help in visualizing and grouping similarly behaving cells. Thus, it is easier for a human expert to discern different states of the network. This makes it possible to perform faster and more efficient troubleshooting and optimization of the parameters of the cells. The presented methods are applicable for different radio access network technologies.

A SOM based approach for visualization of GSM network performance data

In this paper, a neural network based approach to visualize performance data of a GSM network is presented. The proposed approach consists of several steps. First, a suitable proportion of measurement data is selected. Then, the selected set of multi-dimensional data is projected into two-dimensional space for visualization purposes with a neural network algorithm called Self-Organizing Map (SOM). Then, the data is clustered and additional visualizations for each data cluster are provided in order to infer the presence of various failure types, their sources and times of occurrence. We apply the proposed approach in the analysis of degradations in signaling and traffic channel capacity of a GSM network.

A knowledge-based model for analyzing GSM network performance

In this paper, a method to analyze GSM network performance on the basis of massive data records and application domain knowledge is presented. The available measurements are divided into variable sets describing the performance of the different subsystems of the GSM network. Simple mathematical models for the subsystems are proposed. The model parameters are estimated from the available data record using quadratic programming. The parameter estimates are used to find the input-output variable pairs involved in the most severe performance degradations. Finally, the resulting variable pairs are visualized as a tree-shaped cause-effect chain in order to allow user friendly analysis of the network performance.

Analysis of mobile radio access network using the self-organizing map

Mobile networks produce a huge amount of spatio-temporal data. The data consists of parameters of base stations and quality information of calls. The self-organizing map (SOM) is an efficient tool for visualization and clustering of multidimensional data. It transforms the input vectors on a two-dimensional grid of prototype vectors and orders them. The ordered prototype vectors are easier to visualize and explore than the original data. There are two possible ways to start the analysis. We can build either a model of the network using state vectors with parameters from all mobile cells or a general one cell model trained using one cell state vector from all cells. In both methods, further analysis is needed. In the first method the distributions of parameters of one cell can be compared with the others and in the second it can be compared how well the general model represents each cell.

Self-organizing operator maps in complex system analysis

The growth in amount of data available today has encouraged the development of effective data analysis methods to support human decision-making. Neuro-fuzzy computation is a soft computing hybridisation combining the learning capabilities of the neural networks with the linguistic representation of data provided by the fuzzy models. In this paper, a framework to build temporally local neuro-fuzzy systems for the analysis of nonstationary process data using self-organizing operator maps is described.

Outlier Detection in Cellular Network Data Exploration

A cellular network like a GSM network is built up using a number of base stations to cover a large geographical area. The area covered by one base station can be seen as a cell in the network. Regardless of the location of a user the network should be able to provide the services. Therefore each cell should have enough resources to succeed in meeting the demand. Arising from this the analysis of the network can to a certain extent be divided into the analysis of separate cells. In this paper, methods for finding outlying base stations are examined by applying them on the analysis of real data from GSM network. As little as possible prior knowledge of the network is used in the analysis. This way the methods stay more portable into use with data from other networks. The results of the methods are analyzed by comparing them to each other.

A model for optimisation of signal level thresholds in GSM networks

In GSM networks, call blocking, call dropping and bad voice quality are the most common types of service quality problems. The occurrence rate of these problem types is closely connected with the size of the cell served by a BTS. Large cells tend to generate more input traffic and call blocking may occur. Also, the radio signal quality may not be adequate in the border of large cells, causing bad voice quality and call dropping. The size of the cell can be controlled by adjusting, for example, the minimum received signal strength that is required when MSs initiate new calls. In this paper, a model suitable to compute the cell size adjustments that minimise the occurrences of call blocking, call dropping and bad voice quality is presented. The model is based on model estimation from massive data records as well as intensive use of well-known theories of mobile communication.