Wireless communication quality monitoring with artificial neural networks

Quality and reliability of wireless communication is an actual issue for design of modem high-efficiency information systems in the wide area of human activities. In the paper, the problem of wireless communication reliability and methods of its evaluation are studied. The quality of communication at actual spot is estimated with the method proposed by the authors. It is based on the usage of a prediction mathematical model presenting the time series for receiving signal level data. Different model classes are considered for the data description including neural network models. Special model training procedure based on the Aggregative Learning Method (ALM) is applied along with expert approach for the data classification. The validity and the efficiency of the proposed approach have been tested through its application for different cases including "open-air" and "in-building" environments. Cellular phone communication network of DoCoMo Inc. is used as a test bed for the proposed method. Classification abilities of the method are shown reliable for estimation of the communication quality. Characterized with high computational efficiency and simple decision making procedure, the derived method can be useful for design of simple and reliable real-time systems for communication quality monitoring.