AbstractThe use of wireless communication systems is rapidly expanding due to its flexibility, mobility and, low installation and maintenance costs. However as the number of services over wireless networks increase, users expect the same quality of service as on wired networks. Compared to wired channels, a wireless channel is influenced by static and mobile objects in its surroundings. A detailed understanding of these influences are required in order to develop adequate models that can be used to support high data rate services, especially in urban environments where network antennas are frequently installed below surrounding object (building and trees) heights.
The research reported in this thesis was carried out to study the impact of mobile objects (vehicle) on wideband channels in urban environment and the signal variations in a picocell. To conduct this study, a wideband channel sounder that transmits a flat spectrum Pseudo-Random Gaussian Noise (PRGN) signal over a bandwidth of 200 MHz at a carrier frequency of 2 GHz was used. The implemented data acquisition strategy enabled channel measurements to be made every 184.32 μs (equivalent to channel sampling rate of 5.4 kHz). This speed is indispensable for studying fast varying channels. A series of controlled experiment were conducted and reported in this thesis to test the capabilities of the channel sounder and to gain an understanding of the impact of passing vehicles on wideband channels. A number of experimental measurements were then carried out to study static and dynamic channels and the results are reported in this thesis. To gain a detailed understanding of the channel variations, an algorithm that can resolve closely spaced multipath components was required. Singular Value Decomposition Prony (SVDP) algorithm has been developed and tested. Test results show that it achieves time delay resolution of up to 1 ns, a factor of 5 better than Fast Fourier Transform (FFT) algorithm.
Analysis of the signal variations with distance shows that none of the proposed path loss model can be used to predict signal variation with distance in picocell environment, for path length up to 30 m. A linear model is found to provide the best prediction and is proposed for picocell channels. In addition, Rice probability density function is found to provide the best fit to temporal signal variation with increasing standard deviation as the path length increases. The increase in standard deviation is also reflected in the increase of root mean square delay spread and a reduction in channel coherence bandwidth with increasing path length.
Overall, this research has shown that a greater attention needs to be paid to the impact of mobile traffic in urban environments than it was initially thought. Passing vehicles have been shown to cause severe fading within the bandwidth of up to 40 dB but only manifest as between 1 dB and 4 dB fade in the averaged signal power across the bandwidth. This type of fade will introduce error burst in digital communication systems and vary in time and space, especially if either the receiver or object is moving.
In addition to the results mentioned, one of the key contributions of this research is that it shows that a greater attention has to be paid to moving objects in the channel irrespective of their positions relative to the transmitter to receiver path. The sounder used employs technology which allows measurement speeds that have, up until now, not been possible over large bandwidths. Together with SVDP algorithm developed, they present an opportunity for more detailed study to be carried out. This research has also laid the foundation for this to be carried out.
|Date of Award||2015|
|Supervisor||David Ndzi (Supervisor), Rinat Khusainov (Supervisor) & David Sanders (Supervisor)|