IEEE TRANSACTIONS ON COMMUNICATIONS, ACCEPTED FOR PUBLICATION 1 Decision Boundary Evaluation of Optimum and Suboptimum Detectors in Class-A Interference

Abstract-The Middleton Class-A (MCA) model is one of the most accepted models for narrow-band impulsive interference superimposed to additive white Gaussian noise (AWGN). The MCA density consists of a weighted linear combination of infinite Gaussian densities, which leads to a non-tractable form of the optimum detector. To reduce the receiver complexity, one can start with a two-term approximation of the MCA model, which has only two noise states (Gaussian and impulsive state). Our objective is to introduce a simple method to estimate the noise state at the receiver and accordingly, reduce the complexity of the optimum detector. Furthermore, we show for the first time how the decision boundaries of binary signals in MCA noise should look like. In this context, we provide a new analysis of the behavior of many suboptimum detectors such as a linear detector, a locally optimum detector (LOD), and a clipping detector. Based on this analysis, we insert a new clipping threshold for the clipping detector, which significantly improves the bit-error rate performance

Digital communication over fixed time-contin- uous channels with memory- with special application to telephone channels

Digital communication over fixed time- continuous channels with memor

Noise-Enhanced and Human Visual System-Driven Image Processing: Algorithms and Performance Limits

This dissertation investigates the problem of image processing based on stochastic resonance (SR) noise and human visual system (HVS) properties, where several novel frameworks and algorithms for object detection in images, image enhancement and image segmentation as well as the method to estimate the performance limit of image segmentation algorithms are developed. Object detection in images is a fundamental problem whose goal is to make a decision if the object of interest is present or absent in a given image. We develop a framework and algorithm to enhance the detection performance of suboptimal detectors using SR noise, where we add a suitable dose of noise into the original image data and obtain the performance improvement. Micro-calcification detection is employed in this dissertation as an illustrative example. The comparative experiments with a large number of images verify the efficiency of the presented approach. Image enhancement plays an important role and is widely used in various vision tasks. We develop two image enhancement approaches. One is based on SR noise, HVS-driven image quality evaluation metrics and the constrained multi-objective optimization (MOO) technique, which aims at refining the existing suboptimal image enhancement methods. Another is based on the selective enhancement framework, under which we develop several image enhancement algorithms. The two approaches are applied to many low quality images, and they outperform many existing enhancement algorithms. Image segmentation is critical to image analysis. We present two segmentation algorithms driven by HVS properties, where we incorporate the human visual perception factors into the segmentation procedure and encode the prior expectation on the segmentation results into the objective functions through Markov random fields (MRF). Our experimental results show that the presented algorithms achieve higher segmentation accuracy than many representative segmentation and clustering algorithms available in the literature. Performance limit, or performance bound, is very useful to evaluate different image segmentation algorithms and to analyze the segmentability of the given image content. We formulate image segmentation as a parameter estimation problem and derive a lower bound on the segmentation error, i.e., the mean square error (MSE) of the pixel labels considered in our work, using a modified Cramér-Rao bound (CRB). The derivation is based on the biased estimator assumption, whose reasonability is verified in this dissertation. Experimental results demonstrate the validity of the derived bound

Cooperative Communications with Partial Channel State Information in Mobile Radio Systems

Future 4G mobile radio cellular networks are considered OFDM-MIMO systems. Cooperative communication based on coordinated base stations is a very promising concept to perform inter-cell interference management. This thesis deals with the concept of cooperative communication from its information-theoretic background to its practical system design. The main focus is a practical design of the joint detection scheme in the uplink and the joint transmission scheme in the downlink with partial channel-state information (CSI), i.e., significant CSI and imperfect CSI.Zukünftige zellulare 4G-Mobilfunksysteme können als OFDM-MIMO-Systeme betrachtet werden. In solchen zukünftigen Mobilfunksystemen ist kooperative Kommunikation, basierend auf koordinierten Basisstationen, ein sehr vielversprechendes Konzept zum Interzellinterferenzmanagement. Die vorliegende Arbeit behandelt das Konzept der kooperativen Kommunikation vom informationstheoretischen Hintergrund bis hin zum praktischen Systemdesign. Der Schwerpunkt der vorliegenden Arbeit liegt auf dem praktischen Design kooperativer Kommunikationssysteme mit partieller Kanalkenntnis

Detection and Estimation Theory

Contains research objectives and reports on two research projects.Joint Services Electronics Programs (U. S. Army, U.S. Navy, and U.S. Air Force) under Contract DA 28-043-AMC-02536(E)U. S. Navy Purchasing Office Contract N00140-67-C-021

Digital communication over fixed time-continuous channels with memory - with special application to telephone channels.

Bibliography: p. 114-117

A Space Communications Study Final Report, Sep. 15, 1965 - Sep. 15, 1966

Reception of frequency modulated signals passed through deterministic and random time-varying channel