This thesis investigates the optimum detection schemes to mitigate the effects of impulse noise in wireless communication channels. First, we present a measurement campaign to verify the statistical properties of a Middleton Class-A (MCA) model for impulse noise. This campaign measures wireless interference that corrupted a 2.4 GHz industrial, scientific, and medical (ISM) band. We then investigate the optimum detector for binary signals corrupted by MCA noise.
We simplify the MCA model to a single weighted Gaussian density such that the nonlinearities of the optimum detector can be evaluated in a closed-form approximation. By means of such approximations, we introduce a decision boundary analysis to justify and analyze the performance of the optimum detector in different MCA noise environments. As a suboptimum approach, we further approximate the optimum nonlinearities using linear segments to introduce new suboptimum detectors such as a piecewise linear detector and a clipping-like detector.
Next, we extend the approximate MCA model to derive the optimum combining schemes for time and space diversity in the presence of fading and impulse noise. We assume perfect knowledge of noise states to evaluate the analytical performances of the optimum combining schemes for time, receive, and transmit/receive diversity in Rayleigh fading and MCA noise. These evaluations allow us to study the performance loss of spatial diversity with respect to the number of transmit and receive antennas. Finally, we utilize the spectral dimensions in the mitigation problem of impulse noise for orthogonal frequency division multiplexing (OFDM) systems. We present and simplify the optimum receiver of OFDM systems applied to fading channels with MCA noise.
Khodr Saaifan
Impulse noise OFDM systems multiple antenna systems