A Closed-Form Approximated Expression for the Residual ISI Obtained by Blind Adaptive Equalizers with Gain Equal or Less than One

In this paper we propose for the real and two independent quadrature carrier case, a closed-form approximated expression for the achievable residual Inter-Symbol Interference (ISI) that depends on the step-size parameter, equalizer’s tap length, equalized output gain, input signal statistics, channel power and SNR. This expression is valid for blind adaptive equalizers, where the error that is fed into the adaptive mechanism which updates the equalizer‘s taps can be expressed as a polynomial function of order three of the equalized output and where the gain between the input and equalized output signal is less than or equal to one, as is in the case of Godard (gain = 1) and WNEW (gain < 1) algorithm. Since the channel power is measurable or can be calculated if the channel coefficients are given, there is no need to carry out simulation with various step-size parameters in order to reach the required residual ISI. In addition, we show two new equalization methods (gain dependent), which have shown to have improved equalization performance compared to Godard and WNEW

A binaural grouping model for predicting speech intelligibility in multitalker environments

Spatially separating speech maskers from target speech often leads to a large intelligibility improvement. Modeling this phenomenon has long been of interest to binaural-hearing researchers for uncovering brain mechanisms and for improving signal-processing algorithms in hearing-assistive devices. Much of the previous binaural modeling work focused on the unmasking enabled by binaural cues at the periphery, and little quantitative modeling has been directed toward the grouping or source-separation benefits of binaural processing. In this article, we propose a binaural model that focuses on grouping, specifically on the selection of time-frequency units that are dominated by signals from the direction of the target. The proposed model uses Equalization-Cancellation (EC) processing with a binary decision rule to estimate a time-frequency binary mask. EC processing is carried out to cancel the target signal and the energy change between the EC input and output is used as a feature that reflects target dominance in each time-frequency unit. The processing in the proposed model requires little computational resources and is straightforward to implement. In combination with the Coherence-based Speech Intelligibility Index, the model is applied to predict the speech intelligibility data measured by Marrone et al. The predicted speech reception threshold matches the pattern of the measured data well, even though the predicted intelligibility improvements relative to the colocated condition are larger than some of the measured data, which may reflect the lack of internal noise in this initial version of the model.R01 DC000100 - NIDCD NIH HH

A chaotic spread spectrum system for underwater acoustic communication

The work is supported in part by NSFC (Grant no. 61172070), IRT of Shaanxi Province (2013KCT-04), EPSRC (Grant no.Ep/1032606/1).Peer reviewedPostprin

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

Digital Signal Processing on FPGA for Short-Range Optical Communications Systems over Plastic Optical Fiber

Nowadays bandwidth requirements are increasing vertiginously. As new ways and concepts of how to share information emerge, new ways of how to access the web enter the market. Computers and mobile devices are only the beginning, the spectrum of web products and services such as IPTV, VoIP, on-line gaming, etc has been augmented by the possibility to share, store data, interact and work on the Cloud. The rush for bandwidth has led researchers from all over the world to enquire themselves on how to achieve higher data rates, and it is thanks to their efforts, that both long-haul and short-range communications systems have experienced a huge development during the last few years. However, as the demand for higher information throughput increases traditional short-range solutions reach their lim- its. As a result, optical solutions are now migrating from long-haul to short-range communication systems. As part of this trend, plastic optical fiber (POF) systems have arisen as promising candidates for applications where traditional glass optical fibers (GOF) are unsuitable. POF systems feature a series of characteristics that make them very suitable for the market requirements. More in detail, these systems are low cost, robust, easy to handle and to install, flexible and yet tolerant to bendings. Nonetheless, these features come at the expense of a considerable higher bandwidth limitation when compared to GOF systems. This thesis is aimed to the investigate the use of digital signal processing (DSP) algorithms to overcome the bandwidth limitation in short-range optical communications system based on POF. In particular, this dissertation presents the design and development of DSP algorithms on field programmable gate arrays (FPGAs) with the ultimate purpose of implementing a fully engineered 1Gbit/s Ethernet Media Converter capable of establishing data links over 50+ meters of PMMA-SI POF using an RC-LED as transmitte