Nonlinear FXLMS algorithm for active noise control systems with saturation nonlinearity

In active noise control (ANC) applications, the saturation effect of the loudspeaker in the secondary path is considered as the most serious problem that could degrade performance of standard filtered‐x least mean square (FXLMS) control algorithm. When the loudspeaker exhibits nonlinearities, the linear modeling approach fails to identify the secondary path accurately. In the literature, the nonlinear FXLMS (NLFXLMS) algorithm has been proposed to update the ANC controller with a block‐oriented secondary path model. This model consists of nonlinear and linear filters whereby the nonlinear part which represents the saturation effect of the amplifier‐loudspeaker system is modeled by a scaled error function (SEF). The NLFXLMS algorithm requires an exact copy of the linear and nonlinear models of the secondary path. However, NLFXLMS cannot be implemented in real time because the modeling of the SEF cannot be realized. In this paper, a new method to model the secondary path using the Hammerstein model structure and tangential hyperbolic function (THF) is proposed. The THF can represent the SEF to a certain degree of accuracy. Furthermore, the modeling of the THF can be realized using least mean square (LMS) algorithm and utilized in the NLFXLMS control scheme. Simulation results show that the performance of the THF‐based NLFXLMS algorithm is comparable with the SEF‐based NLFXLMS. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Event storm detection and identification in communication systems

Event storms are the manifestation of an important class of abnormal behaviors in communication systems. They occur when a large number of nodes throughout the system generate a set of events within a small period of time. It is essential for network management systems to detect every event storm and identify its cause, in order to prevent and repair potential system faults.This paper presents a set of techniques for the effective detection and identification of event storms in communication systems. First, we introduce a new algorithm to synchronize events to a single node in the system. Second, the system's event log is modeled as a normally distributed random process. This is achieved by using data analysis techniques to explore and then model the statistical behavior of the event log. Third, event storm detection is proposed using a simple test statistic combined with an exponential smoothing technique to overcome the non-stationary behavior of event logs. Fourth, the system is divided into non-overlapping regions to locate the main contributing regions of a storm. We show that this technique provides us with a method for event storm identification. Finally, experimental results from a commercially deployed multimedia communication system that uses these techniques demonstrate their effectiveness.