Markov Modeling of Third Generation Wireless Channels
Wireless has proved to be one of the most important and fastest growing fields of communications especially during last few decades. To achieve reliable communication, we model a wireless system to analyze its performance and to find ways to improve the reliability of a particular system. Extensive research is being done to accurately model wireless systems, and to achieve better performance. Simulation techniques have been in use for many years to support the design and evaluation of electronic communication systems. Over the past few decades, Computer Aided Design (CAD) techniques (including both computerized analytical techniques and simulation) have matured, and are now usually applied at some point in the system design/development process.
The aim of this thesis is to find efficient algorithms that can model third generation wireless channels in a discrete sense. For modeling these channels, mathematical tools known as hidden Markov models are used. These models have proved themselves to be very efficient in many areas of electrical engineering including speech recognition, pattern recognition, artificial intelligence, wavelets and queuing theory. Wideband Code Division Multiple Access (W-CDMA) wireless communication parameters including channels fading statistics, Bit Error Rate (BER) performance and interval distribution of errors are modeled using different Markov models, and their results are tested and validated.
Four algorithms for modeling error sources are implemented, and their results are discussed. Both hidden Markov models and semi-hidden Markov models are used in this thesis, and their results are validated for the W-CDMA environment. The state duration distributions for these channels are also approximated using Phase-Type (PH) distribution.
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