Robust nonparametric spectral estimation includes generating an accurate estimate of the Power Spectral Density (PSD) for a given set of data while trying to minimize the bias due to data outliers. Robust nonparametric spectral estimation is applied in the domain of electrical communications and digital signal processing when a PSD estimate of the electromagnetic spectrum is desired (often for the goal of signal detection), and when the spectrum is also contaminated by Impulsive Noise (IN). Power Line Communication (PLC) is an example of a communication environment where IN is a concern because power lines were not designed with the intent to transmit communication signals. There are many different noise models used to statistically model different types of IN, but one popular model that has been used for PLC and various other applications is called the Middleton Class A model, and this model is extensively used in this thesis. The performances of two different nonparametric spectral estimation methods are analyzed in IN: the Welch method and the multitaper method. These estimators work well under the common assumption that the receiver noise is characterized by Additive White Gaussian Noise (AWGN). However, the performance degrades for both of these estimators when they are used for signal detection in IN environments. In this thesis basic robust estimation theory is used to modify the Welch and multitaper methods in order to increase their robustness, and it is shown that the signal detection capabilities in IN is improved when using the modified robust estimators. / Master of Science / One application of blind spectral estimation is blind signal detection. Unlike a car radio, where the radio is specifically designed to receive AM and PM radio waves, sometimes it is useful for a radio to be able to detect the presence of transmitted signals whose characteristics are not known ahead of time. Cognitive radio is one application where this capability is useful. Often signal detection is inhibited by Additive White Gaussian Noise (AWGN). This is analogous to trying to hear a friend speak (signal detection) in a room full of people talking (background AWGN). However, some noise environments are more impulsive in nature. Using the previous analogy, the background noise could be loud banging caused by machinery; the noise will not be as constant as the chatter of the crowd, but it will be much louder. When power lines are used as a medium for electromagnetic communication (instead of just sending power), it is called Power Line Communication (PLC), and PLC is a good example of a system where the noise environment is impulsive. In this thesis, methods used for blind spectral estimation are modified to work reliably (or robustly) for impulsive noise environments.

Power line communication has been around in past decades and gained renewed attention thanks to the demand of high-speed Internet access. With the significant advantages of existing infrastructure and accessibility to even remote areas, power grid has become one of the promising competitors for multi-media transmission in household. However, the power line was not oriented for data transmission providing a rather hash environment. To overcome the difficulties, advanced modulation and channel coding schemes should be employed. In the thesis low density parity check code (LDPC) is employed to reduce the loss caused by various kinds of effects in the channel especially the noise since its performance approaches to Shannon capacity limit. Moreover, OFDM multi-carrier transmission technique is involved which could decrease the inter-symbol interference and frequency selective fading. Nevertheless, LDPC decoding process was designed specifically for the common Gaussian white noise condition, combined with OFDM modulation the system still could not provide satisfying and practicable performance so improvements are needed for the system. The main works of the thesis are as follows. Set up an environment of power line transmission investigating and simulating the channel characteristics; employ multi-path channel model and Class‐A noise model for further developing the improvement algorithms to deal with the selective fading and impulse noise. Two algorithms proposed here are from different perspectives: the first one is modifying initial posterior information for LDPC decoding and the second one aims at suppressing the impulse noise after demodulation. Finally, a few simulations are performed to reveal the effectiveness of proposed methods. As a result, the improved scheme shows a great superiority improving the performance by no less than 5dB compared to traditional system.