Performance of blind equalization with higher order statistics in indoor radio environments

This paper analyzes the performance of blind equalization using the complex cepstrum of third-order moments applied to 4-QAM time division multiple access (TDMA) indoor radio communication systems. In particular, we have modeled a dispersive indoor channel with Rice statistics. We used the blind algorithms to estimate the channel-impulse response, and from this, we computed the equalizer coefficients using a classical minimum mean square error (MMSE) algorithm. In order to evaluate the system performance, we calculated the bit error rate (BER) of a decision feedback equalizer (DFE) that uses a tricepstrum algorithm to estimate the channel-impulse response. The results are compared with those obtained using a least sum of square errors (LSSE) algorithm as a channel estimator and considering the exact channel response. The results obtained show that this kind of blind equalizer performs better than the more classically trained equalizer when Rice channels with a strong direct path and signal-to-noise ratios (SNR’s) lower than 20 dB are taken into account. However, some problems relating to the length of time needed for convergence must be solved.Peer Reviewe

Self-correcting multi-channel Bussgang blind deconvolution using expectation maximization (EM) algorithm and feedback

A Bussgang based blind deconvolution algorithm called self-correcting multi-channel Bussgang (SCMB) blind deconvolution algorithm was proposed. Unlike the original Bussgang blind deconvolution algorithm where the probability density function (pdf) of the signal being recovered is assumed to be completely known, the proposed SCMB blind deconvolution algorithm relaxes this restriction by parameterized the pdf with a Gaussian mixture model and expectation maximization (EM) algorithm, an iterative maximum likelihood approach, is employed to estimate the parameter side by side with the estimation of the equalization filters of the original Bussgang blind deconvolution algorithm. A feedback loop is also designed to compensate the effect of the parameter estimation error on the estimation of the equalization filters. Application of the SCMB blind deconvolution framework for binary image restoration, multi-pass synthetic aperture radar (SAR) autofocus and inverse synthetic aperture radar (ISAR) autofocus are exploited with great results.Ph.D.Committee Chair: Dr. Russell Mersereau; Committee Member: Dr. Doug Willams; Committee Member: Dr. Mark Richards; Committee Member: Dr. Xiaoming Huo; Committee Member: Dr. Ye (Geoffrey) L

Design and Development of Intelligent Sensors

In this project, we make an extensive study of Intelligent Sensors and devise methods for analyzing them through various proposed algorithms broadly classified into Direct and Inverse Modeling. Also we look at the analysis of Blind Equalization in any sensor. A regular sensor is a device which simply measures a signal and converts it into another signal which can be read by an observer and an instrument. A sensor's sensitivity indicates how much the sensor's output changes when the measured quantity changes. Ideal sensors are designed to be linear. The output signal of such a sensor is linearly proportional to the value of the measured property. The sensitivity is then defined as the ratio between output signal and measured property. For example, if a sensor measures temperature and gives a voltage output, the sensitivity is a constant with the unit [V/K]; this sensor is linear because the ratio is constant at all points of measurement. If the sensor is not ideal, several types of deviations can occur which render the sensor results inaccurate. On the other hand, an intelligent sensor takes some predefined action when it senses the appropriate input (light, heat, sound, motion, touch, etc.).A sensor is intelligent when it is capable of correcting errors occurred during measurement both at the input and output ends. It generally processes the signal by means of suitable methods implemented in the device before communicating it. As we discussed an ideal sensor should have linear relationship with the measures quantity. But since in practice there are several factors which introduce non-linearity in a system, we need intelligent sensors. This particular project concentrates on the compensation of difficulties faced due to the non-linear response characteristics of a capacitive pressure sensor (CPS).It studies the design of an intelligent CPS using direct and inverse modeling switched-capacitor circuit(SCC) converts the change in capacitance of the pressure-sensor into an equivalent voltage output . The effect of change in environmental conditions on the CPS and subsequently on the output of the SCC is such that it makes the output non-linear in nature. Especially change in ambient temperature causes response characteristics of the CPS to become highly nonlinear, and complex signal processing may be required to obtain correct results. The performance of the control system depends on the performance of the sensing element. It is observed that many sensors exhibit nonlinear input-output characteristics. Due to such nonlinearities direct digital readout is not possible. As a result we are forced to employ the sensors only in the linear region of their characteristics. In other words their usable range gets restricted due to the presence of nonlinearity. If a sensor is used for full range of its nonlinear characteristics, accuracy of measurement is severely affected. Similar effect is also observed in case of LVDT. The nonlinearity present is usually time-varying and unpredictable as it depends on many uncertain factors. Nonlinearity also creeps in due to change in environmental conditions such as temperature and humidity. In addition ageing of the sensors also introduces nonlinearity. The proposed scheme incorporates intelligence into the sensor. We use many algorithms and ANN models to make the sensor ‘intelligent’. Also there is an analysis of the Blind Deconvolution Techniques that maybe used for Channel Estimation. As it is a relatively new field of work, the challenges are huge but opportunities are many as well. We try to make sensors more intelligent as they would allow a varied application of them in industry, academic and domestic environments

On issues of equalization with the decorrelation algorithm : fast converging structures and finite-precision

To increase the rate of convergence of the blind, adaptive, decision feedback equalizer based on the decorrelation criterion, structures have been proposed which dramatically increase the complexity of the equalizer. The complexity of an algorithm has a direct bearing on the cost of implementing the algorithm in either hardware or software. In this thesis, more computationally efficient structures, based on the fast transversal filter and lattice algorithms, are proposed for the decorrelation algorithm which maintain the high rate of convergence of the more complex algorithms. Furthermore, the performance of the decorrelation algorithm in a finite-precision environment will be studied and compared to the widely used LMS algorithm

Estimation récursive des cumulants d'Ordre quatre avec application à l'identification

Les Statistiques d'Ordre Élevé (SOE) sont de plus en plus utilisées dans les applications de traitement du signal. Toutefois, des formules générales d'estimation récursive des cumulants d'ordre supérieur à trois font jusqu'à aujourd'hui défaut. Cet article comble en partie cette lacune en présentant une formule récursive pour l'estimation des cumulants d'ordre quatre. Cette formule est ensuite utilisée dans le cadre de l'identification de modèles paramétriques de type Réponse Impulsionnelle Finie (RIF). Une version moindres carrés de l'algorithme C(Q,k), basée sur cette formule, est proposée. Des résultats de simulation illustrant le comportement de cette méthode d'identification sont présentés

Blind detection in channels with intersymbol interference

In high speed digital transmission over bandlimited channels, one of the principal impairments, besides additive white Gaussian noise, is intersymbol interference. For unknown channels, adaptive equalization is used to mitigate the interference. Different types of equalizers were proposed in the literature such as linear, decision feedback equalizers and maximum likelihood sequence estimation. The transmitter embeds sequences with the data regularly to help the equalizer adapt to the unknown channel parameters. It is not always appropriate or feasible to send training sequences; in such cases, self adaptive or blind equalizers are used. The past ten years have witnessed an interest in the topic. Most of this interest, however, was devoted to linear equalization In this dissertation we concentrate on blind decision feedback equalization and blind maximum likelihood sequence estimation. We propose a new algorithm: the decorrelation algorithm, for controlling the blind decision feedback equalizer. We investigate properties such as convergence and probability of error. A new algorithm is also proposed for blind maximum likelihood sequence estimation. We use two trellises: one for the data and the other for the channel parameters. The Viterbi algorithm is used to search the two trellises for the best channel and data sequence estimates. We derive an upper bound for this scheme. We also address the problem of ill convergence of the constant modulus algorithm and propose a technique to improve its convergence. Using this technique, global convergence is guaranteed as long as the channel gain exceeds a certain critical value. The question of the Viterbi algorithm\u27s complexity is important for both conventional and blind maximum likelihood sequence estimation. Therefore, in this dissertation, the problem of reducing the complexity of the Viterbi algorithm is also addressed. We introduce the concept of state partitioning and use it to reduce the number of states of the Viterbi algorithm. This technique offers a better complexity/performance tradeoff than previously proposed techniques

Space-Time Block Coding for Wireless Communications

Abstract Wireless designers constantly seek to improve the spectrum efficiency/capacity, coverage of wireless networks, and link reliability. Space-time wireless technology that uses multiple antennas along with appropriate signalling and receiver techniques offers a powerful tool for improving wireless performance. Some aspects of this technology have already been incorporated into various wireless network and cellular mobile standards. More advanced MIMO techniques are planned for future mobile networks, wireless local area network (LANs) and wide area network (WANs). Multiple antennas when used with appropriate space-time coding (STC) techniques can achieve huge performance gains in multipath fading wireless links. The fundamentals of space-time coding were established in the context of space-time Trellis coding by Tarokh, Seshadri and Calderbank. Alamouti then proposed a simple transmit diversity coding scheme and based on this scheme, general space-time block codes were further introduced by Tarokh, Jafarkhani and Calderbank. Since then space-time coding has soon evolved into a most vibrant research area in wireless communications. Recently, space-time block coding has been adopted in the third generation mobile communication standard which aims to deliver true multimedia capability. Space-time block codes have a most attractive feature of the linear decoding/detection algorithms and thus become the most popular among different STC techniques. The decoding of space-time block codes, however, requires knowledge of channels at the receiver and in most publications, channel parameters are assumed known, which is not practical due to the changing channel conditions in real communication systems. This thesis is mainly concerned with space-time block codes and their performances. The focus is on signal detection and channel estimation for wireless communication systems using space-time block codes. We first present the required background materials, discuss different implementations of space-time block codes using different numbers of transmit and receive antennas, and evaluate the performances of space-time block codes using binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), and quadrature amplitude modulation (QAM). Then, we investigate Tarokh’s joint detection scheme with no channel state information thoroughly, and also propose a new general joint channel estimation and data detection scheme that works with QPSK and 16-QAM and different numbers of antennas. Next, we further study Yang’s channel estimation scheme, and expand this channel estimation scheme to work with 16-QAM modulation. After dealing with complex signal constellations, we subsequently develop the equations and algorithms of both channel estimation schemes to further test their performances when real signals are used (BPSK modulation). Then, we simulate and compare the performances of the two new channel estimation schemes when employing different number of transmit and receive antennas and when employing different modulation methods. Finally, conclusions are drawn and further research areas are discussed