Optimal pilot waveform assisted modulation for ultrawideband communications

Ultrawideband (UWB) transmissions induce pronounced frequency-selective fading effects in their multipath propagation. Multipath diversity gains can be collected to enhance performance, provided that the underlying channel can be estimated at the receiver. To this end, we develop a novel pilot waveform assisted modulation (PWAM) scheme that is tailored for UWB communications. We select our PWAM parameters by jointly optimizing channel estimation performance and information rate. The resulting transmitter design maximizes the average capacity, which is shown to be equivalent to minimizing the mean-square channel estimation error, and thereby achieves the Crame/spl acute/r-Rao lower bound. Application of PWAM to practical UWB systems is promising because it entails simple integrate-and-dump operations at the frame rate. Equally important, it offers a flexible UWB channel estimator, capable of striking desirable rate-performance tradeoffs depending on the channel coherence time.     

Bootstrapping the Local Periodogram of Locally Stationary Processes

Abstract. Locally stationary processes are non‐stationary stochastic processes the second‐order structure of which varies smoothly over time. In this paper, we develop a method to bootstrap the local periodogram of a locally stationary process. Our method generates pseudo local periodogram ordinates by combining a parametric time and non‐parametric frequency domain bootstrap approach. We first fit locally a time varying autoregressive model so as to capture the essential characteristics of the underlying process. A locally calculated non‐parametric correction in the frequency domain is then used so as to improve upon the locally parametric autoregressive fit. As an application, we investigate theoretically the asymptotic properties of the bootstrap method proposed applied to the class of local spectral means, local ratio statistics and local spectral density estimators. Some simulations demonstrate the ability of our method to give accurate estimates of the quantities of interest in finite sample situations and an application to a real‐life data‐set is presented.     

Object and texture classification using higher order statistics

The problem of the detection and classification of deterministic objects and random textures in a noisy scene is discussed. An energy detector is developed in the cumulant domain by exploiting the noise insensitivity of higher order statistics. An efficient implementation of this detector is described, using matched filtering. Its performance is analyzed using asymptotic distributions in a binary hypothesis-testing framework. The object and texture discriminant functions are minimum distance classifiers in the cumulant domain and can be efficiently implemented using a bank of matched filters. They are immune to additive Gaussian noise and insensitive to object shifts. Important extensions, which can handle object rotation and scaling, are also discussed. An alternative texture classifier is derived from a ML viewpoint and is statistically efficient at the expense of complexity. The application of these algorithms to the texture-modeling problem is indicated, and consistent parameter estimates are obtained     

Joint coding-precoding with low-complexity turbo-decoding

We combine error-control coding with linear precoding (LP) for flat-fading channels, as well as for wireless orthogonal frequency-division multiplexing transmissions through frequency-selective fading channels. The performance is analyzed and compared with the corresponding error-control-coded system without precoding. By wedding LP with conventional error-control coding, the diversity order becomes equal to the error-control code's minimum Hamming distance times the precoder size. We also derive a low-complexity turbo-decoding algorithm for joint coded-precoded transmissions. We analyze the decoding complexity and compare it with an error-control-coded system without LP. Extensive simulations with convolutional and turbo codes for HiperLan/2 channels support the analysis and demonstrate superior performance of the proposed system.     

Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks

UWB technology provides an excellent means for wireless positioning due to its high resolution capability in the time domain. Its ability to resolve multipath components makes it possible to obtain accurate location estimates without the need for complex estimation algorithms. In this article, theoretical limits for TOA estimation and TOA-based location estimation for UWB systems have been considered. Due to the complexity of the optimal schemes, suboptimal but practical alternatives have been emphasized. Performance limits for hybrid TOA/SS and TDOA/SS schemes have also been considered. Although the fundamental mechanisms for localization, including AOA-, TOA-, TDOA-, and SS-based methods, apply to all radio air interface, some positioning techniques are favored by UWB-based systems using ultrawide bandwidths.     

Higher-order statistics-based input/output system identification and application to noise cancellation

Higher-than-second-order statistics-based input/output identification algorithms are proposed for linear and nonlinear system identification. The higher-than-second-order cumulant-based linear identification algorithm is shown to be insensitive to contamination of the input data by a general class of noise including additive Gaussian noise of unknown covariance, unlike its second-order counterpart. The nonlinear identification is at least as optimal as any linear identification scheme. Recursive-least-squares-type algorithms are derived for linear/nonlinear adaptive identification. As applications, the problems of adaptive noise cancellation and time-delay estimation are discussed and simulated. Consistency of the adaptive estimator is shown. Simulations are performed and compared with the second-order design.Part of the results of this paper were presented at the workshop on HOSA, Vail, CO, June 1989, and at the International Conference on ASSP, Albuquerque, NM, April 1990. The work of G. B. Giannakis in this paper was supported by LabCom Contract 5-25254.     

Image modeling using inverse filtering criteria with application totextures

Statistical approaches to image modeling have largely relied upon random models that characterize the 2-D process in terms of its first- and second-order statistics, and therefore cannot completely capture phase properties of random fields that are non-Gaussian. This constrains the parameters of noncausal image models to be symmetric and, therefore, the underlying random field to be spatially reversible. Research indicates that this assumption may not be always valid for texture images. In this paper, higher- than second-order statistics are used to derive and implement two classes of inverse filtering criteria for parameter estimation of asymmetric noncausal autoregressive moving-average (ARMA) image models with known orders. Contrary to existing approaches, FIR inverse filters are employed and image models with zeros on the unit bicircle can be handled. One of the criteria defines the smallest set of cumulant lags necessary for identifiability of these models to date, Consistency of these estimators is established, and their performance is evaluated with Monte Carlo simulations as well as texture classification and synthesis experiments.     

New facets of mobile botnet: architecture and evaluation

It is without a doubt that botnets pose a growing threat to the Internet, with DDoS attacks of any kind carried out by botnets to be on the rise. Nowadays, botmasters rely on advanced Command and Control (C&C) infrastructures to achieve their goals and most importantly to remain undetected. This work introduces two novel botnet architectures that consist only of mobile devices and evaluates both their impact in terms of DNS amplification and TCP flooding attacks, and their cost pertaining to the maintenance of the C&C channel. The first one puts forward the idea of using a continually changing mobile HTTP proxy in front of the botherder, while the other capitalizes on DNS protocol as a covert channel for coordinating the botnet. That is, for the latter, the messages exchanged among the bots and the herder appear as legitimate DNS transactions. Also, a third architecture is described and assessed, which is basically an optimized variation of the first one. Namely, it utilizes a mixed layout where all the attacking bots are mobile, but the proxy machines are typical PCs not involved in the actual attack. For the DNS amplification attack, which is by nature more powerful, we report an amplification factor that fluctuates between 32.7 and 34.1. Also, regarding the imposed C&C cost, we assert that it is minimal (about 0.25 Mbps) per bot in the worst case happening momentarily when the bot learns about the parameters of the attack.