This study considers the meteor-burst communication (MBC) environment at three levels. At the lowest level, the trails themselves are studied and analysed. Then individual links are studied in order to determine the data throughput and wait time that might be expected at various data rates. Finally, at the top level, MBC networks are studied in order to provide information on the effects of routing strategies, topologies, and connectivity in such networks. A significant amount of theoretical work has been done in the classification of meteor trails, and the analysis of the throughput potential of the channel. At the same time the issues of wait time on MBC links, and MBC network strategies, have been largely ignored. The work presented here is based on data captured on actual monitoring links, and is intended to provide both an observational comparison to theoretical predictions in the well-researched areas, and a source of base information for the others. Chapter 1 of this thesis gives an overview of the field of meteor-burst communications. Prior work in the field is discussed, as are the advantages and disadvantages of the channel, and current application areas. Chapter 2 describes work done on the classification of observed meteor trails into distinctive 'families'. The rule-based system designed for this task is discussed as well as the eventual classification schema produced, which is far more comprehensive and consistent than previously proposed schemas. Chapter 3 deals with the throughput potential of the channel, based on the observed trails. A comparison to predicted results, both as regards fixed and adaptive data-rates, is made with some notable differences between predicted v results and observed results highlighted. The trail families with the largest contribution to the throughput capacity of the channel are identified. Chapter 4 deals with wait time in meteor-burst communications. The data rates at which wait time is minimised in the links used are found, and compared to the rates at which throughput was optimised. These are found to be very different, as indeed are the contributions of the various trail families at these rates. Chapter 5 describes a software system designed to analyse the effect of routing strategies in MBC networks, and presents initial results derived from this system. Certain features of the channel, in particular its sporadic nature, are shown to have significant effects on network performance. Chapter 6 continues the presentation of network results, specifically concentrating on the effect of topologies and connectivity within MBC networks. Chapter 7 concludes the thesis, highlighting suggested areas for further research as well as summarising the more important results presented. / Thesis (Ph.D.)-University of Natal, Durban, 1991.

In meteor-burst communications systems, the channel is bursty with a continuously fluctuating signal-to-noise ratio. Adaptive data rate systems attempt to use the channel more optimally by varying the bit rate. Current adaptive rate systems use a method of closed-loop decision-feedback to control the transmitted data rate. It is proposed that an open-loop adaptive data rate system without a decision feedback path may be possible using implicit channel information carried in the first few milliseconds of the link establishment probe signal. The system would have primary application in low-cost half-duplex telemetry systems. It is shown that the key elements in such a system would be channel predictors. The development of these predictors is the focus of this research. Two novel methods of predicting channel parameters are developed. The first utilises early fast Doppler information that precedes many long duration, large signal-to-noise-ratio overdense trails. The presence of early fast Doppler at the trail commencement is used as a toggle to operate at a higher data rate. Factors influencing the use of early fast Doppler for this purpose are also presented. The second method uses artificial neural networks. Data measured during trail formation is processed and presented to the neural networks for prediction of trail parameters. Several successful neural networks are presented which predict trail type, underdense or overdense, and peak trail amplitude from the first 50ms of the trail's lifetime. This method allows better estimation of the developing trail. This fact can be used to implement a multi-rate open-loop adaptive data rate system. / Thesis (Ph.D.)-University of Natal, Durban, 1994.