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The recent emphasis on Information Technology has increased the need for methods of data communications with a greater interest in the areas of Satellite communications. Data communications over a satellite can be easily achieved by the use of excessive power and bandwidth but efficient management of the satellite resource requires more elegant means of transmission. The optimum modulator and demodulator can be described by mathematical expressions to represent the physical processes that are required to transmit and receive a signal. Digital Signal Processing circuits can be use to implement these mathematical functions and once correctly designed are not susceptible to variations in accuracy and hence can maintain an accurate representation of the mathematical model. This thesis documents an investigation into the algorithms and techniques that can be used in the digital implementation of a Satellite Data Modem. The technique used for carrier phase recovery and data decoding is a major variation on a method proposed by Viterbi and Viterbi and relies on Phase Estimation instead of the more common carrier regeneration techniques. A computer simulation of this algorithm and its performance is described and the overall performance of the simulation is compared to theoretical analysis and experimental performance of a Multi-Data Rate Satellite Modem covering data rates in the range 16 Ksymbol/sec to 256 Ksymbol/sec in both the BPSK and QPSK data formats.

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This research investigates communications network routing procedures, based on distributed learning automata concepts for circuit and packet switched networks. For this application, the learning automaton is shown to be an ideal adaptive control mechanism, with simple feedback and updating strategies which allow extremely practical implementations and perform very close to the desired optimum. In this thesis, the nature of learning automata routing schemes are explored by analytical and computer simulation techniques, primarily developing an elementary understanding of the automata routing and adaption process. Using simple circuit and message switched networks the conditions for minimum blocking probability and average delay are established and compared with the equilibrium behaviour of learning automata operating under alternative reinforcement algorithms. Later, large scale simulations of real networks are used to demonstrate and relate the learning automata scheme to existing routing techniques. These experiments, which are performed on sophisticated simulation packages produced for this study, take as examples hierarchical and general structured telephone networks and packet switched communications networks configured with both virtual call and datagram protocols. In addition, studies under failure mode conditions, including link, node and focussed overloads, conclusively demonstrate the superior performance afforded by the learning automata routing approach.

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