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Reliable communication in non-Gaussian environments : receiver design and analytical aspects Mitra, Jeebak


With the plethora of devices that operate in current communication networks, there is a non-zero probability that radio frequency signals from disparate sources may interfere with each other and therefore one has to contend with unwanted signals that corrupt the desired signal. The unwanted part, collectively referred to as noise, may be attributed to a number of factors ranging from device irregularities to varied ambient phenomena. Traditionally by applying the central limit theorem, noise in communication systems has been characterized by a Gaussian distribution. However, it has been recognized time and again, that in plenty of cases this is an abstraction of the real characteristics of the noise since for a variety of reasons the central limit theorem may not hold true for the observed noise. Such noise is generally referred to as being non-Gaussian. The general belief about non-Gaussian noise is that it deteriorates signal fidelity, resulting in unreliable communication. However, the loss in reliability is due to the fact that almost all communication systems are designed to well handle Gaussian noise and hence suffers loss when this assumption is not true. We characterize the performance of coded and uncoded communication systems in non-Gaussian noise. More specifically we consider robust decoding techniques when the noise is impulsive and is correlated. We incorporate the effect of non-ideal interleaving on system performance when the noise has memory and provide several design recommendations for such environments. We also propose techniques to acquire information on the statistics of the noise when it can be modeled as a Markovian-Gaussian process and analyse the performance of such estimators. These techniques are then applied to contemporary technologies such as cognitive transmission and impulse radio ultra wideband transmission, as a proof of concept, and to quantify the benefits that exist in accurately characterizing the interference in such systems. Furthermore, we use spatial diversity in mitigating the effects of non-Gaussian noise through a distributed multi-antenna approach. Better known as cooperative diversity, this approach is shown to require careful design when the facilitating nodes are affected by strong interference and we provide novel algorithms for the same.

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