Baseband digital communication in electro-magnetic measurement while drilling (EM-MWD) systems is often corrupted by non-white surface noise. The inability to reliably decode the transmitted signals in a noisy environment limits the depth at which EM-MWD systems can operate. Correlation receivers, which are optimal in the presence of additive white Gaussian noise, can be sub-optimal in the presence of various types of field noise at different drilling sites. This thesis investigates the application of artificial neural networks (ANN) as communication receivers in EM-MWD baseband digital communication systems. The performances of various ANN architectures and training algorithms are studied and compared with conventional correlation receivers via computer simulations. Standard symbol error rate (SER) test results show that the NN receiver is able to adapt to site-specific noise and thus outperforms the traditional correlation receiver.

Ultra-wideband (UWB) communications is expected to revolutionize high data-rate, short-distance wireless communications, providing data-rates in excess of 100 Mbps. However, the wireless channel distorts the transmitted signal by dispersing the signal energy over time. This degrades the output signal-to-noise ratio (SNR) of a correlation based matched-filter receiver, limiting the achievable data-rate and user capacity. Most wideband channel models do not account for all the identified dispersion mechanisms namely the frequency dispersion, the resonant dispersion and the multipath dispersion. The objective of this research is to model resonant dispersion based on the Singularity Expansion Method (SEM) and provide guidelines for UWB receiver design to meet the data capacity. The original contribution of this research is a novel pole dispersion channel model that includes resonant dispersion characterization. An empirical investigation supports our claim that a correlation type matched-filter receiver using a template signal based on the pole dispersion channel model overcomes distortion related losses. Various physical mechanisms responsible for dispersion in UWB communication systems are described in detail. The applicability of the proposed dispersive channel model is evaluated using the optimal matched filter (OMF) receiver. The SEM approach, which was originally proposed for target identification using short pulse radars, offers limited benefits of due to its susceptibility to noise. A combined fuzzy-statistical approach is proposed to improve the robustness of resonant dispersion channel modeling in presence of noise. A natural extension of this doctoral research is to improve buried landmine detection as well as breast tumor detection by applying statistical and fuzzy analysis to the backscatter response. Moreover, radar target identification using UWB short pulses stands to gain tremendously from this research. / Ph. D.

Abstract This Thesis examines low complexity receiver structures for impulse-radio (IR) ultra-wideband (UWB) systems to be used in wireless sensor network applications. Such applications require radio communication solutions characterized by low cost, low complexity hardware and low power consumption to provide very long battery life. Analysis of several auto-correlation receiver (AcR) structures is performed in the presence of additive white Gaussian noise to identify receiver structures that offer a good compromise between implementation complexity and data communication performance. The classes of receiver that demonstrate the best complexity/performance trade-off are shown to be the AcR utilising transmitted-reference with binary pulse amplitude modulation signaling, and the energy detector (ED) utilising binary pulse position modulation. The analysis of these two schemes is extended to consider multipath fading channels. Numerically integrable bit error rate probability (BEP) expressions are derived in order to evaluate the receivers' performance in the presence of fading distributions characterized by closed form characteristic functions. Simulations utilising widely accepted UWB channel models are then used to evaluate the BEP in different indoor environments. Since UWB systems share frequency spectrum with many narrowband (NB) systems, and need to coexist with other UWB systems, the performance of low complexity receivers can be seriously affected by interference. In the presence of NB interference, two cases have been considered: 1) single NB interference, where the interfering node is located at a fixed distance from the receiver, and 2) multiple NB interference, where the interfering nodes are scattered according to a spatial Poisson process. When considering UWB interference, the case of multiple sources of interference has been considered. For both the multiple NB and the multiple UWB interference cases, the model derived considers several interference parameters, which can be integrated into BEP formulations for quick performance evaluations. The framework is sufficiently simple to allow tractable analysis and can serve as a guideline for the design of heterogeneous networks where coexistence between UWB systems and NB systems is of importance. The very large bandwidth of UWB signals offers an unprecedented possibility for accurate ranging operations. Signal leading-edge estimation algorithms based on average maximum likelihood estimators are derived considering different multipath channel fading distributions. Suboptimal solutions are proposed and investigated in order to support ranging capabilities in low complexity receiver structures. The ability to identify line-of-sight and non-line-of-sight conditions with the ED-based receiver is also addressed. An example of an IR-UWB low complexity transceiver based on ED for sensor network applications is proposed in this Thesis. Ad-hoc solutions for pulse transmission, synchronization and data detection are developed.

This thesis deals with the demonstration of bit error rate reduction techniques. In the theory section, different techniques of bit error rate reduction are presented, followed by the introduction of the communication channel and the communication system parameters. Each technique is simulated in Matlab Simulink environment using the additive white Gaussian noise channel model. The simulations are captured in waveform oscillograms of all the signal processing stages. The practical section of this thesis offers a functional laboratory solution demonstrating different ways of transmission: a direct-path signal, a path with a cross-correlation receiver and a path using matched filtering. The individual circuits are described in detail, including waveform oscillograms of the signal processing blocks for illustration. Furthermore, the practical section presents printed circuit boards designs, comprehensive documentation for production, measurement results and a sample laboratory protocol.