Symmetric alpha-Stable Adapted Demodulation and Parameter Estimation

Abstract: Transmission and reception of signals in wireless communication systems is affected by additive interference corrupting the signal. Traditionally, the interference is assumed to be AWGN and the system designs are usually based on that assumption. Modern military platforms consists of many electrical components and systems and as such the noise affecting the signals is often a product of interference between the components and systems. This type of noise tend to be very impulsive in nature. The standard AWGN model is not suited for impulsive noise which leaves an opportunity to investigate the performance of a demodulation scheme adapted to the current interference environment in order to increase the performance gain. To properly analyze the performance of an interference-adapted demodulator, knowledge about the characteristic parameters of the chosen noise model is required to perform the necessary calculations.  This project combines the aspect of adaptive demodulation with parameter estimation evaluation. Four different parameter estimation techniques specifically customized for Symmetric alpha-Stable distributed noise were implemented and examined. The four methods were the Empirical Characteristic Function (ECF) method, Fractional Lower-Order Moments (FLOM) method, Extreme-Order Statistics (EOS) method as well as the Quantiles method. The effectiveness and performance of the methods were investigated in two Symmetric alpha-Stable processes of varying level of impulsiveness as well as two Class A processes in order to monitor the performance in noise not specifically distributed according to the intended model, functioning as an arbitrary representation of non-Gaussian interference. The results were evaluated using the measure of Kullback-Leibler Divergence. The demodulator was designed for Symmetric alpha-Stable distributed noise and implemented using an LLR-algorithm. The simulations were performed using an LDPC-coding protocol and the experiment was conducted in both Class A and Symmetric alpha-Stable distributed noise. The modulation schemes were 4-QAM and BPSK. The simulations showed that ECF was the most consistent parameter estimation method overall, regardless of distribution model or number of available samples. FLOM performed well in alpha-Stable noise but struggled in Class A processes. EOS and Quantiles shared the struggles of fewer available samples. The experiments show that an alpha-Stable adapted demodulator coupled with a parameter estimation technique based on the empirical characteristic function (ECF) is a very competitive and viable option in impulsive interference environments regardless of the origin of the noise distribution. The performance gain vis-a-vis demodulation using the standard AWGN option exceeded thresholds of upwards 25 dB for impulsive noise processes.