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The Global ETD Search service is a free service for researchers
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Multiple-input multiple-output optical wireless communicationsTran, Tuan-Anh January 2013 (has links)
Visible-light optical wireless communications (OWC) is a potential technology that can help resolve the crowdedness of the radio-frequency bands, whilst conveniently exploiting energy-saving light-emitting diodes (LEDs) as transmitters for both illumination and communications. Since there usually are many LEDs in a lighting unit, OWC has a multi- input multi-output (MIMO) geometry which, thanks to its channel diversity, can offer wireless local networks at data-rates many times higher than possible with single-channel systems. In such systems, MIMO-detection methods to separate the different optical channels play an important role in improving the system performance by helping reduce cross-talk between channels. To measure the performance of a particular geometry for MIMO communications, a simulation study, reported in this thesis, found that, amongst the signal- independent metrics, the condition number may be used as a rough predictor of the performance, whilst the channel Signal-to-Interference-and-Noise Ratio (SINR) is the most appropriate for geometry assessment. Combined with the fact that the overall performance of a MIMO system is mostly dominated by its worst channel, this indicates that the most effective way to improve the system performance is to maximise the worst channel’s SINR. One of the possible solutions to improving the SINRs is to use holograms to steer the transmitter images such that their distributions over the photo-detectors reduce overlaps. As LEDs emit partially-coherent light, the beam steering has to be carried out with partially- coherent illumination. By using two lenses to parallelise and collect partially-coherent light before and after the hologram, respectively, the source and image intensity distributions, and the autocorrelation of the hologram can be related in a succinct mathematical relationship. This leads to the development of three computational algorithms based on the autocorrelation function to obtain a quantised hologram with the desired beam-steering capability. These algorithms have their cost functions and performance comparison done at the hologram plane instead of the image plane, which therefore takes less time than traditional image-based methods. Specifically, one of these algorithms is able to save significant time over both the other autocorrelation-based algorithms and the direct binary-search, by 33% and by 50% respectively. A simulation-based study and a corresponding experiment, both reported in this thesis, found that the one of the proposed algorithms had poor power efficiency, whilst the other two were both highly effective in generating digital holograms with precise and power-efficient beam-steering performance. Of these two algorithms, one had superior time performance and was likely the best of the three proposed autocorrelation-based algorithms for generating beam-steering holograms. MIMO-OWC simulation also demonstrated the capability of using beam-steering holograms to design the channel and improve the system performance. Combining reported findings, a strategy can be devised to optimise the throughput of an imaging MIMO-OWC system for a given transmitted power.
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Channel Probing for an Indoor Wireless Communications ChannelHunter, Brandon 13 March 2003 (has links) (PDF)
The statistics of the amplitude, time and angle of arrival of multipaths in an indoor environment are all necessary components of multipath models used to simulate the performance of spatial diversity in receive antenna configurations. The model presented by Saleh and Valenzuela, was added to by Spencer et. al., and included all three of these parameters for a 7 GHz channel. A system was built to measure these multipath parameters at 2.4 GHz for multiple locations in an indoor environment. Another system was built to measure the angle of transmission for a 6 GHz channel. The addition of this parameter allows spatial diversity at the transmitter along with the receiver to be simulated. The process of going from raw measurement data to discrete arrivals and then to clustered arrivals is analyzed. Many possible errors associated with discrete arrival processing are discussed along with possible solutions. Four clustering methods are compared and their relative strengths and weaknesses are pointed out. The effects that errors in the clustering process have on parameter estimation and model performance are also simulated.
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