Linear Signal Processing Algorithms Research Articles

Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals

Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, whereas the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical signal to noise ratio estimation and modulation format classification, respectively. The proposed methods accurately evaluate optical signals employing up to 64 quadrature amplitude modulation, at 32 Gbd, using only directly detected data.

Modeling and Mitigation of Narrowband Interference for Transmitted-Reference UWB Systems

Transmitted-reference ultra-wideband (TR-UWB) systems, in conjunction with autocorrelation-receivers (AcR), can collect all energy of the multipath channel response at low complexity. Unfortunately the AcR front-end also collects the energy of narrowband interference (NBI) signals, which severely degrades its performance. We present an elaborate statistical analysis of the NBI for a multichannel AcR front-end. Our results show specific correlation properties, which can be exploited to mitigate the NBI by post-processing the AcR output samples. This explains how linear signal processing algorithms can be used to efficiently suppress the cross-terms arising in the nonlinear AcR between all input signals: the UWB, NBI, and noise signals. Linear least squares (LS) and minimum mean squared error (MMSE) receivers are investigated. To support our analysis, computer simulations are conducted using an IEEE802.11a WLAN signal as NBI.

An integrated sensory-intelligent system for underwater acoustic signal-processing applications

A generic integrated sensory-intelligent system (ISIS) is developed for underwater acoustic signal-processing applications. ISIS constantly monitors the current acoustic channel conditions and smoothly integrates the outputs of the most appropriate signal-processing procedures or algorithms available to it for those conditions. The system is based on a generalization of a tuneable approximate piecewise linear (TAPL) model derived from the modified probabilistic neural network (MPNN). This model was designed to seamlessly integrate a set of local linear signal-processing algorithms within a given multidimensional data space. Depending on the input signal distortions, which are determined by environmental effects, ISIS automatically weighs and adds the outputs from a set of processing algorithms working in parallel. The weighting is related to the closeness of each algorithm to the sensed input signal characteristics or some other measured environmental state. A single tuning parameter is used to smoothly and seamlessly select appropriately among the parallel processing algorithm outputs. A very small tuning-parameter value selects the closest most appropriate algorithm output. At the other extreme, a fixed weighted average of all the algorithm outputs is produced with a very large value. Otherwise, a dynamic weighed average of all algorithm outputs is achieved with values in between. Some features and benefits of ISIS are demonstrated with an illustrative linear sweep chirp signal-detector estimation problem characterized by extremely variable Doppler conditions.

Spectroscopy and hybrid neural network analysis

This paper reviews the current use of spectroscopy and related instrumentation in chemical analysis. Advancements in digital signal processing technology are making it possible to improve the sensitivity and accuracy of analytical instruments without expensive upgrading of instrument hardware. A hybrid neural network (HNN) is described that can perform nonlinear signal analysis. The HNN approach combines the simple data reduction capability of conventional linear signal processing algorithms with the adaptive learning and recognition ability of a multilayer nonlinear neural network architecture. A number of examples show the rise of the HNN for environmental monitoring and real-time process control.