Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.768951
Title: A statistical model for the dual polarised MIMO land mobile satellite channel at S-band
Author: Ni Mhearain, Fiona Sinead Dunwoody
ISNI:       0000 0004 7656 0354
Awarding Body: Heriot-Watt University
Current Institution: Heriot-Watt University
Date of Award: 2018
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Abstract:
This thesis explores channel modelling approaches to the land mobile satellite (LMS) channel in S-band, focussing on the implementation of multiple input multiple output techniques through the use of dual polarisation. An Enhanced Statistical Model is presented and the output of this model is analysed and compared to the two current state-of-the-art models that simulate the dual polarised LMS channel, i.e. the statistical Liolis-CTTC model and the geometric ray-tracing QuaDRiGa model. The enhanced model builds on the Liolis-CTTC model and presents solutions to a number of issues that arise in the statistical modelling process. The enhancements in the new model include imposing temporal correlation on the slow variations without unwanted high frequency components from low-pass filtering, introducing Doppler effects including Doppler shaping of the fast variations, implementing a smooth state transition process and also implementing an interpolation process to sample the channel at the required sub-symbol rate for transmission. In addition to the analysis of the three models, real channel measurements of the dual polarised LMS channel from the MIMOSA campaign are analysed. A statistical comparison between the models and the real measurement data for simulated journeys in a number of user environments is conducted through analysis of the timeseries, the cumulative density function (CDF), average fading duration (AFD) and level-crossing rate (LCR). Capacity analysis and eigenvalue analysis is also conducted and allows for validation of the enhanced model. The comparisons with the measurement data show good agreement between the real measurement data and the enhanced model.
Supervisor: Sellathurai, Mathini Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.768951  DOI: Not available
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