Error probabilities over generalized-K fading channels

In assessing the performance of wireless communication systems, various models are used to analyse the extent of fading and shadowing of signals during transmission from its source to the receiver. Observations are done on the parameters affecting the extent of fading and shadowing such as the amoun...

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Bibliographic Details
Main Author: Nurul Farhana Abdul Razak
Other Authors: Li Kwok Hung
Format: Final Year Project
Language:English
Published: 2019
Subjects:
Online Access:http://hdl.handle.net/10356/78097
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Institution: Nanyang Technological University
Language: English
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Summary:In assessing the performance of wireless communication systems, various models are used to analyse the extent of fading and shadowing of signals during transmission from its source to the receiver. Observations are done on the parameters affecting the extent of fading and shadowing such as the amount of fading, signal-to-noise ratio, and bit error rate. While many models exist, each of the models come with their respective limitations. General limitations found on older models are the inability to provide concurrent observation of both fading and shadowing, high complexity of the mathematical expressions, and complex concepts which they adopt. This report looks at reasons why a particular model known as the generalized-K distribution is favoured among the models available. It then aims to explore the reliability of the generalized-K distribution by employing the model in assessing the performance of a wireless communication system. In expanding the extent of the study, this report tests the reliability of the generalized-K distribution on less commonly used modulations schemes such as BFSK and DPSK. In testing the system performance in generalized-K, we can conclude that generalized-K has allow us to use a simpler and easier form rather than the complicated existing mathematical form. Not only it can approximate other existing composite model well, but it also has reduced the complexity of existing composite model.