Secrecy capacity is a fundamental information-theoretic performance metric to predict the maximum data rate of reliable communication, while the intended message is not revealed to the eavesdropper. Motivated by this consideration in this paper, a unified communication-theoretic framework for the analysis of the probability of nonzero secrecy capacity, the secrecy outage probability, and the secrecy capacity of multiple-antenna systems over fading channels is proposed. Specifically, a powerful frequency-domain approach is first developed in which the integrals involved in the evaluation of the probability of nonzero secrecy capacity and secrecy outage probability are transformed into the frequency domain, by employing Parseval's theorem. A generic approach for the evaluation of the asymptotic secrecy outage probability at high signal-to-noise ratio (SNR) region is also introduced, thus providing useful insight as to the parameters affecting the secrecy performance. Finally, a unified numerical approach for computing the average secrecy capacity of multiple-antenna systems under arbitrary fading environments is developed. The proposed framework is general enough to accommodate any well-known multiantenna transmission technique and fading model. Finally, the secrecy performance of several multiple-antenna system setups is assessed, in the presence of generalized fading conditions and arbitrary antenna correlation, while various numerical and computer simulation results are shown and compared to substantiate the proposed mathematical analysis.

The correlated bivariate K-distribution with arbitrary and not necessarily identical parameters is introduced and analyzed. Novel infinite series expressions for the joint probability density function and moments are derived for the general case where the associated bivariate distributions, i.e., Rayleigh and gamma, are both arbitrary correlated. These expressions generalize previously known analytical results obtained for identical parameter cases. Furthermore, considering independent gamma distributions, the cumulative distribution and characteristic functions are analytically obtained. Although the derived expressions can be used in a wide range of applications, this letter focuses on the performance analysis of dual branch diversity receivers. Specifically, the outage performance of dual selection diversity receivers operating over correlated K fading/shadowing channels is analytically evaluated. Moreover, for low normalized outage threshold values, closed-form expressions are obtained.

Orthogonal frequency division multiplexing (OFDM) is an emerging multi-carrier modulation scheme, which has been adopted for several wireless standards such as IEEE 802.11a and HiperLAN2. A well-known problem of OFDM is its sensitivity to frequency offset between the transmitted and received carrier frequencies. This frequency offset introduces inter-carrier interference (ICI) in the OFDM symbol. In this letter, we investigate two methods for combating the effects of ICI: the extended Kalman filter (EKF) method and a form of the sequential Monte Carlo (SMC) method called sequential importance sampling (SIS). Through simulations, we explore the efficiency of these two methods for various frequency offsets and different signal-to-noise ratios (SNRs). Our estimates of the frequency offset are very satisfactory, especially in the latter case, resulting in performance improvement of the OFDM modulation scheme.

A nonparametric generalization of the locally optimum Bayes (LOB) parametric theory of signal detection in additive non-Gaussian noise with independent sampling is presented. From a locally asymptotically normal (LAN) expansion of the log-likelihood ratio the nonparametric detector structure, in both coherent and incoherent modes, is determined. Moreover, its statistics under both hypotheses are obtained. The nonparametric LAN log-likelihood ratio is then reduced to a least informative (i.e., having minimum variance under the null hypothesis, H/sub 0/) local parametric submodel, which is referred to as adaptive. In the adaptive submodel, certain nonlinearities are replaced by their efficient estimates. This is accomplished such that no information is lost when the noise first-order density is no longer parametrically defined. Adaptive nonparametric LOB detectors are thus shown to be asymptotically optimum (AO), canonical in signal waveform, distribution free in noise statistics, and identical in form (in the symmetric cases) to their parametric counterparts. A numerical example is provided when the underlying density is Middleton's (see ibid., vol.45, p.1129-49, May 1999)Class-A noise, which demonstrates that even with a relatively small sample size (O(10/sup 2/)) adaptive LOB nonparametric detectors perform nearly as well as the classical LOB detectors.

The purpose of this paper is to extend the recent formulation of locally optimum Bayes (LOB) detection in nonadditive non-Gaussian noise with independent sampling to the case where dependence between the noise samples is modeled via an ergodic first-order Markov discrete-time process. Moreover, unlike previous related work, numerical results are provided which are based on an empirically derived second-order transition density, the marginal PDF of which is Middleton's (1993) Class A noise, an experimentally verifiable and widely applicable non-Gaussian model. Canonical, in signal waveform and noise statistics, asymptotically and LOB detectors in both coherent and incoherent modes are derived and their statistics are computed under both "signal present" and "signal absent" hypotheses. Performance measures are thus obtained together with the correlation gain G/sup (M.P.)/, which is used for systems comparison. Explicit forms for the transition density and and the nonlinearities involved, as well as numerical values of the noise indices, are calculated from a generalized observation model containing multiplicative and additive Markov noise components. It is shown that significant performance gains over the case with independent sampling can be achieved, depending upon the degree of correlation between the noise samples.

The locally optimum Bayes detection (LOBD) framework for nonadditive non-Gaussian noise is applied to a particular additive and signal-dependent noise model where the noise components are correlated. Under the proposed observation model, the performance of the LOB detectors is compared with that of well-known conventional receivers.

The locally optimum Bayes theory of signal detection in additive non-Gaussian noise/interference is extended to independent observations of the received data without the additive noise restriction. The methodology employed parallels very closely the original development of threshold detection theory and utilizes the mathematical machinery of asymptotic decision theory, especially, the concepts of contiguity and locally asymptotically normal (LAN) log-likelihood ratio, which are needed in the determination of the detector structure in both coherent and incoherent modes and its statistics under both hypotheses. Under the present framework, the canonical (in signal waveform and noise statistics) optimum detection algorithms retain their asymptotically optimum character. An example is provided in order to demonstrate the applicability of the theory to a specific noise environment, where explicit forms of the non-linearities involved and numerical values of the new noise indices are obtained. Moreover, a significant improvement in performance (0 (24-27) dB) over that of optimum detectors in independent, additive Gaussian and non-Gaussian noise is noted.>

The locally optimum Bayes theory of signal detection in non-Gaussian noise/interference environments is extended to include ergodic Markov noise models under mild regularity assumptions on the conditional probability density functions. The proposed method expresses the log-likelihood ratio under the null hypothesis, via martingale limit theory, as a locally asymptotically normal likelihood ratio, which yields under the implied condition of contiguity the statistics of the detection algorithm under the alternative hypothesis. Thus, optimum detection algorithms in both coherent and incoherent cases are obtained, which are canonical in signal waveform and noise statistics and which have the desired property of asymptotic optimality (acceptably small error probabilities as sample size becomes necessarily large, while the terms in the Taylor expansion of the log-likelihood ratio about the null signal remain fixed). Furthermore, locally optimum detection structures in Gauss-Markov noise are given together with a specific example in the coherent mode of reception in order to demonstrate the significant improvement in performance obtained over independent sampling.>

Detection algorithms that are locally optimum Bayes, and also asymptotically optimum, are developed for both coherent and incoherent signaling for arbitrary interference and signal waveforms when the dependence in the noise samples is represented by a moving-average model. This leads to receiver structures, which are prewhitened versions of the locally optimum detectors in the independent case. A probability-of-error expression (in the ideal-observer symmetric case), the processing gain, and the minimum-detectable signal are derived in both cases. These demonstrate, by means of an expression comparing performance between this and the independent case, that for the same large sample size (n>>1), an improvement in performance is always achieved when the noise samples are dependent, without any additional complexity in receiver structure.>

The weak-signal receiver for binary differential phase-shift keying (DPSK) in additive noise is derived, and its performance in terms of the error probability in the most general narrow-band impulsive (nonGaussian) noise model, Middleton's class A noise, is analyzed.