Fade Depth Prediction Using Human Presence for Real Life WSN Deployment

Current problem in real life WSN deployment is determining fade depth in indoor propagation scenario for link power budget analysis using (fade margin parameter). Due to the fact that human presence impacts the performance of wireless networks, this paper proposes a statistical approach for shadow fading prediction using various real life parameters. Considered parameters within this paper include statistically mapped human presence and the number of people through time compared to the received signal strength. This paper proposes an empirical model fade depth prediction model derived from a comprehensive set of measured data in indoor propagation scenario. It is shown that the measured fade depth has high correlations with the number of people in non-line-of-sight condition, giving a solid foundation for the fade depth prediction model. In line-of-sight conditions this correlations is significantly lower. By using the proposed model in real life deployment scenarios of WSNs, the data loss and power consumption can be reduced by the means of intelligently planning and designing Wireless Sensor Network

Fabrication Errors Influence on the Sperical Array Radiation Pattern

The paper studies fabrication errors influence on the radiation pattern of a spherical antennas array. The developed Moment Method (MoM) program analyzes a particular influence of an azimuth and elevation position and local angle errors of antenna elements as well as model dimension errors on the radiation pattern. The spectral domain approach to the analysis of the spherical antenna arrays is applied here. Finally, results obtained from the theoretical investigation are verified by comparison with measured results