Wetlands are vital for maintaining ecosystems and supporting biodiversity, but they face increasing threats from climate change and human activities. Accurate mapping of wetlands is essential to detect detrimental changes and guide effective conservation efforts. However, many wetland mapping (WM) methods using convolutional neural networks rely on kernels with fixed sizes and shapes, limiting their ability to capture the multiscale features of wetlands. To enhance their ability, we propose Multiscale Deformable DenseNet (MDD) by integrating deformable convolutions into the DenseNet architecture and employing a dual feature extractor. The deformable convolutions adapt kernel shapes and sampling to capture spatial patterns across scales, while the dual feature extractor uses varied kernel sizes for diverse receptive fields. These innovations significantly improve the classification accuracy for the complex WM task, where classes are often highly similar. Experimental results demonstrate that MDD achieves the highest overall accuracy (OA) in three hyperspectral data sets, with an OA of 97.24%, 98.23%, and 94.59%, compared to the best competing OAs of 96.33%, 97.16% and 92.70%, respectively. These results highlight MDD’s superiority in WM1.

Non-stationary signals in several applications appear sparse in the time-frequency (TF) domain. In this letter, we optimize a quadratic TF distribution (QTFD) versus its kernel by exploiting this property by maximizing power per nonzero components in the TFD domain. This approach reduces the number of large cross-terms (CTs) values while preserving auto-terms. This optimization is conducted by introducing an efficient approximation of$\ell _{0}$and$\ell _{1}$norm without any assumption about auto-terms' locations and CTs in the TF domain. The proposed TFD exhibits superior performance in instantaneous frequency (IF) estimation and fewer CTs than the state-of-the-art TFDs.

In this paper, we optimize a Wireless Information and Power Transfer (WIPT) system including multiple pairs of users, where transmitters employ a single-antenna to transmit their information and power to their receivers with the help of one multiple-antenna Amplify-and-Forward (AF) relay or an active Intelligent Reflecting Surface (IRS). We propose a joint Time Switching (TS) scheme in which transmitters, receivers, and the relay/IRS are either in their energy or information transmission/reception modes. The transmitted multi-carrier unmodulated and modulated waveforms are used for Energy Harvesting (EH) and Information Decoding (ID) modes, respectively. In order to design an optimal fair system, we maximize the minimum rate of all pairs for both relay and IRS systems through a unified framework. This framework allows us to simultaneously design energy waveforms, find optimal relay/IRS amplification/reflection matrices, allocate powers for information waveforms, and allocate time durations for various phases. This problem turns out to be non-convex. Thus, we propose an iterative algorithm by using the Minorization-Maximization (MM) technique, which efficiently converges to the optimized solution, i.e., stationary point of the problem (under mild conditions). Numerical examples show that the proposed method improves the performance of the multi-pair WIPT relay/IRS system under various setups.

In this paper, we propose a novel hyperspectral unmixing through the perturbed linear mixture model to take into account the spectral variability offset of the linear mixture model. In our proposed approach, we reformulate the LMM by adding a term to account for the spectral variations of endmember spectra of the dictionary. We use a white Additive Gaussian distribution for the perturbations in the LMM and employ the maximum likelihood estimation. Our proposed Augmented Gaussian LMM (AGLMM) employs the multiplicative updating rules to accelerate the convergence and exploit the sparsity of the unknown parameters. We evaluate our proposed unmixing approach on different datasets. Our results show the superior performance of the proposed AGLMM method over the state-of-the-art methods.

Coprime array can augment the array aperture and enhance the interferences suppression capability for array processing. In this paper, we propose a coprime virtual iterative adaptive approach (CVIAA) method to achieve beamforming performance improvement. Existing coprime array adaptive beamformers (CAABs) usually select the maximum virtual uniform linear array to estimate parameters with the non-uniform virtual sensors deleted, which will suffer from performance loss because some information is ignored. The proposed CVIAA method can cope with the non-uniformity of virtual coarray where entire data is preserved. Even though the virtual array signal is a single snapshot, the proposed CVIAA method can precisely estimate the powers of source signals and noise variance in an iterative way. Moreover, the proposed CVIAA estimator outperforms the existing virtual array Capon estimator when the source signals are closely located. Unlike the traditional iterative adaptive approach (IAA), the proposed CVIAA method reduces the computational complexity by dividing the data into several low-dimension subsets and estimates the noise variance iteratively. Finally, we calculate the weight vector of the proposed CAAB from its definition. Computer simulations verify that the proposed CVIAA estimator is superior to existing estimators and the proposed CAAB can handle environmental uncertainties.

This letter presents a high resolution method which separates close components of a multi-component linear frequency modulated (LFM) signal and eliminates their Cross-Terms (CTs). We first investigate the energy distribution of the Auto-Terms (ATs) and CTs in ambiguity plane. This reveals that the energy of the CTs of parallel close components is significant around the origin. We propose to mask the samples in which the CTs may have interferences with the ATs. This mask is signal-dependent and its directions are determined using the relationship between the radial slices of ambiguity function (AF) and the fractional Fourier transform (FrFT). Exploiting sparsity in time-frequency (TF) domain and by solving an ℓ1-norm minimization problem, the localized time-frequency distribution (TFD) is extracted from the acquired samples of the AF. Simulation results reveal significant improvements in the efficiency compared to previous works.

While pitch detection has been the focal subject of numerous research efforts for several decades, it is still a challenging task in noisy conditions. In this article, we propose a method to improve the pitch detection accuracy of conventional pitch detection methods. The proposed pitch detection process starts with using the pitch value estimated by a conventional pitch detection method. Then, it extracts pitch candidates according to the most probable types of errors in the initial estimation of high-pitch and low-pitch frames classified by a Deep Convolutional Neural Network (DCNN). Next, a restrained selection procedure is run to find the true pitch value from the set of pitch candidates. In this procedure, we employ two features (harmonic summation and Euclidian deviation), the soft decision of the DCNN, the pitch smoothness feature in successive frames, and the effect of the initial estimation in a cost function. The pitch value which leads to the lowest cost value is chosen as the estimated pitch value. The simulations on CSTR and KEELE databases, in noisy environments with twelve types of noise, were performed. The results show the superiority of the proposed method over the state-of-the-art methods under different SNR conditions.

In this paper, we consider a cognitive radio system with some of secondary users (SUs) equipped with multiple antenna full-duplex transceivers. We design a hybrid system to sense and access the spectrum opportunistically such that the SU operates on both underlay and interweave hybrid methods while the primary user (PU) may be active or inactive, respectively, to perform the sensing as agile as possible. Moreover, we maximize the throughput by allocating the spectral resources assuming limited available energy during each time frame with constraints on the interference to the primary user. We show that this problem is convex and propose a simple solution algorithm for it. Our simulations confirm the accuracy of our analytical and theoretical results and illustrate that the total achieved throughput of full-duplex cognitive radio even by considering self-interference is maximized under the assumed conditions.

In this paper, we study the spectrum sensing problem when the sensing device is equipped with an uncalibrated multiantenna array. We consider an unknown Toeplitz structure for the covariance matrix of the signal of interest and unknown uncorrelated noise variances. We propose two new invariant constant false-alarm rate detectors with less computational cost than the generalized likelihood ratio test: (1) the Rao test and (2) a separating function estimation test (SFET). We calculate approximate expressions for the false-alarm and detection probabilities of the proposed Rao test-based detector. In addition, we derive the exact false-alarm probability of the proposed SFET for the case of two antennas. Moreover, we obtain an accurate approximation for the false-alarm probability of the proposed SFET for the general case using the method of moments. The accuracy of these expressions is validated by Monte-Carlo experiments. The simulation results show that the proposed detectors outperform state-of-the-art detectors.

In this study, the authors propose an algorithm for tracking of a mobile user and jointly estimate fading parameters of the wireless environment, e.g. the path‐loss exponent. The authors use a state space dynamic model for the mobility of the device with a discrete Markov process for its acceleration and exploit only the received signal strength indication (RSSI) from multiple anchor points. They use particle filters for tracking, a maximum likelihood estimator for the path‐loss exponent, and a Kalman filter for estimating of the shadow fading. The sequence of estimation procedures is done in one time step. The simulation comparison with similar methods illustrates a significant performance improvement by estimating the unknown fading parameters of RSSI.

In this letter, we propose a new accurate approach for target localization in multiple-input multiple-output (MIMO) radars, which exploits the sparse spatial distribution of targets to reduce the sampling rate. We express the received signal of a MIMO radar in terms of the deviations of target parameters from the grid points in the form of a block sparse signal using the expansion around all the neighbor points. Applying a block sparse recovery method, we can estimate both the grid-point locations of targets and these deviations. The proposed approach can yield more accurate localization with higher detection probability compared with its counterparts. Moreover, the proposed approach can reduce the computational complexity.

In this letter, we propose a sparsity promoting feedback acquisition and reconstruction scheme for sensing, encoding and subsequent reconstruction of spectrally sparse signals. In the proposed scheme, the spectral components are estimated utilizing a sparsity-promoting, sliding-window algorithm in a feedback loop. Utilizing the estimated spectral components, a level signal is predicted and sign measurements of the prediction error are acquired. The sparsity promoting algorithm can then estimate the spectral components iteratively from the sign measurements. Unlike many batch-based compressive sensing algorithms, our proposed algorithm gradually estimates and follows slow changes in the sparse components utilizing a sliding-window technique. We also consider the scenario in which possible flipping errors in the sign bits propagate along iterations (due to the feedback loop) during reconstruction. We propose an iterative error correction algorithm to cope with this error propagation phenomenon considering a binary-sparse occurrence model on the error sequence. Simulation results show effective performance of the proposed scheme in comparison with the literature.

We investigate a partially blind channel estimation and symbol detection in an amplify-and-forward two way relay scheme. We derive generalized likelihood ratio test detectors for reciprocal and non-reciprocal channels, whose computational complexity (CC) grow exponentially with the block length. Therefore, we propose a convergent iterative method for joint channel estimation and symbol detection with CC proportional to the block length. We also address the convergence proof. We further propose the simple blind and partially-blind approaches based on least squares (LS) and weighted LS estimation, respectively, for operation at low SNRs and analyze their mean square errors (MSE). Our simulations reveal the effectiveness of the proposed method compared with its counterparts.

Two‐way relaying tends to become a celebrated technique in communication networks because of its unique characteristic in establishing a two‐step bilateral information exchange mechanism between users' pairs, as well as, arranging a virtual spatial diversity scenario. In this paper, two‐way relaying under amplify‐and‐forward strategy, and fast Rayleigh fading environment is considered. We investigate two distinct cases in which the relay retransmits users' superposed signals, either directly or after complex conjugation in two‐ or three‐phase modalities. We derive the optimal detection rule which is inherently computational complex. Two closed‐form suboptimal structures are proposed to circumvent this formidable problem. In the first form, we incorporate the destination noise into the relay counterpart, and assume Gaussian conditional distribution for the received signal in the latter. The first detector is designed using less stringent approximation and consequently has a better performance, whereas the second one benefits from a very low computational complexity and simple implementation and is more suitable for applications with strict computational and energy constraints such as sensor networks. Furthermore, we consider the imperfect self‐channel information case and extend the corresponding results. Bit error rate analyses and accompanying computer simulations show an improvement of about 5 dB over existing methods.

In this study, the authors present a method to derive sequential detector (SD) for a general class of composite hypothesis problems. The authors first explain how the SD is constructed by employing a ‘weight function’. Then, the authors employ this method in the cooperative spectrum sensing (SS) in cognitive radio networks where the primary user transmits a phase shift keying (PSK) signal with unknown complex amplitude in additive white Gaussian noise. The noise power is assumed known in the first scenario and unknown in the second one. To evaluate the performance of the resulting SDs, the authors obtain the required average sample number (ASN) function to meet the bounds of false alarm and missed detection probabilities through some numerical evaluations. The results illustrate that the average sensing delay of the proposed SDs are less than the required number of observations in the traditional fixed sample size detectors. In the proposed SDs, the authors also demonstrate that the increase of signal‐to‐noise ratio leads to decrease of ASN.

The problem of multiple antenna spectrum sensing in cognitive radio (CR) networks is studied in this paper. We propose two new invariant constant false-alarm rate eigenvalue-based (EVB) detectors, using the higher order moments of the sample covariance matrix eigenvalues, by exploiting the separating function estimation test framework. We find closed-form expressions for the false-alarm and detection probabilities of the proposed detectors by providing moment-based approximations of their statistical distributions. The accuracy of the obtained closed-form expressions is validated by Monte Carlo simulations. In addition, we compare the performance of the proposed detectors with that of their two counterparts, i.e., John's and the arithmetic to geometric mean (AGM) detectors, in terms of the asymptotic relative efficiency. This comparison enables us to demonstrate the superiority of our proposed detectors over those detectors within the typical range of signal-to-noise ratio in CR application. The comparative simulation results also illustrate the superiority of the proposed detectors over John's and the AGM detectors as well as some other state-of-the-art EVB algorithms given in the literature.

In this paper, we demonstrate that Gaussian mixture distribution is a suitable model for the number of received molecules when on-off keying (OOK) modulation scheme is used in diffusion-based Molecular Communication (MC) systems. Using the proposed Gaussian mixture model, we design a receiver by minimizing the error probability. An iterative algorithm is derived to determine a threshold value which minimizes a linear approximation of the error probability at each iteration. Numerical evaluations reveal that our theoretical analysis based on the Gaussian mixture model match our simulation very closely. Furthermore, we propose a memory-based receiver in which previously detected symbols are considered in deriving the detection algorithm. Computer simulations confirm that the memory-based receiver effectively eliminates inter-symbol interference (ISI) and significantly improves the bit error rate (BER) performance of the system.

Transmission of information in nano-networks can be implemented by the emission and reception of molecules in diffusion-based molecular communications. Since the propagation environment is shared among multiple nodes, the molecules emitted by different transmitters cause interuser interference (IUI). In this paper, we propose molecular code division multiple access (MCDMA) to overcome the IUI caused by transmitted signals from asynchronous users. Based on adopting on-off keying (OOK) modulation scheme in implementing molecular codes, we develop a chip detection scheme with an adaptable threshold to decide between “on” and “off” hypotheses, by counting the number of received molecules. To eliminate the inter-symbol interference (ISI) caused by residual molecules, the receiver is modified by exploiting previously detected chips. The performance evaluation of the proposed MCDMA system via computer simulations show that the symbol error rate (SER) of the receiver considering previously detected chip outperforms the memory-less receiver. Results indicate that the MCDMA system is a promising approach to reduce interference created by asynchronous users in multi-user molecular communications.

In this paper, we introduce an algorithm for estimating and tracking the pitch period of audio signals using Bayesian filters. For this purpose, we propose a general Bayesian model, which is robust to the nonstationary variations of the amplitude and frequency of the input signal. We also employ a state-space model, which uses the delayed versions of the input signal to model the periodicity of nonstationary audio signals. This simple model allows a significant reduction of the required number of particles for the estimation of the pitch period compared to the state-of-the-art particle filtering methods. Moreover, we propose to estimate the logarithm of the period instead of the period itself. We show that the resulting algorithm does not require prior knowledge about the initial state and is robust to the octave error phenomenon, which is a common problem in pitch period estimation methods. Most of the existing methods require that the processing window be longer than the largest existing period of the input signal. In contrast, the proposed method does not impose such a limit. Our method often results in a higher time-domain resolution with no perceptible compromise on the frequency-domain resolution, especially for high-pitched audio signals such as music. Simulation results reveal that the proposed algorithm outperforms the state-of-the-art pitch period detection algorithms at low signal to noise ratios assuming no prior knowledge about the initial conditions.

We investigate non-coherent detection of $M$ -ary orthogonal signaling in a denoise-and-forward two-way relay scheme in fast fading, as well as block fading channels. We first design the maximum likelihood, as well as maximum a-posteriori detectors, and then propose a simple suboptimal version for this digital network coding system in Rayleigh fast fading environment. We find exact expression of bit error rate for this suboptimal detector. Interestingly, this suboptimal detector performs only less than 0.25 dB away from optimum, i.e., this expression is a tight upper bound for the performance of the optimal one. Moreover, we derive the optimal detector for the case where the channel amplitudes are known at the relay. To make this detector practical, we propose efficient estimators for the channel amplitudes at the relay for block fading environments. Finally, our simulations reveal the effectiveness of the proposed methods compared with the state-of-the-art method.

In this paper, we consider an exponentially windowed sample covariance matrix (EWSCM) and propose an improved estimator for its eigenvalues. We use new advances in random matrix theory, which describe the limiting spectral distribution of the large dimensional doubly correlated Wishart matrices to find the support and distribution of the eigenvalues of the EWSCM. We then employ the complex integration and residue theorem to design an estimator for the eigenvalues, which satisfies the cluster separability condition, assuming that the eigenvalue multiplicities are known. We show that the proposed estimator is consistent in the asymptotic regime and has good performance in finite sample size situations. Simulation results show that the proposed estimator outperforms the traditional estimator, significantly.

In this paper, we propose a new detector for multiantenna spectrum sensing in cognitive radios (CR) by exploiting the Separating Function Estimation Test (SFET) framework. Specifically, we consider a blind scenario for multiantenna spectrum sensing in which both the channel gains and noise variance are assumed to be unknown. For such a scenario, we find an appropriate Separating Function (SF) whose Maximum Likelihood Estimate (MLE) leads us to a SFET-based detector which uses the second order moments of the eigenvalues of the Sample Covariance Matrix (SCM). We also find closed-form expressions for the detection and false-alarm probabilities of the proposed detector. The performance of the proposed detector asymptotically tends to that of the Uniformly Most Powerful Unbiased (UMPU) detector as the number of independent and identically distributed observations increases. In addition, simulation results show that the proposed detector outperforms the state-of-art eigenvalue- based detectors because of using the second order moments of the SCM eigenvalues.

This paper presents an algorithm for estimating the instantaneous fundamental frequency of a noisy non‐stationary periodic signal whose components are harmonically related. To this end, the authors’ propose a harmonic state‐space model for the input signal and use it to derive an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). In this model, the input signal is characterised by a time‐varying fundamental frequency and amplitude which is a practical assumption for real‐world periodic signals. In contrast to most of existing methods such as short‐time Fourier transform, the proposed algorithm does not use any windowing technique. Therefore the trade‐off between time and frequency resolutions is less controversial and so can be used for real‐time frequency tracking. It also reveals some fine and continuous variations in signal pitch such as Vibrato and Glissando. Simulation results show that the proposed algorithm performs well even when most of the signal energy is contained in the higher‐order harmonics. The performance of the proposed algorithm using EKF, UKF and PF is also evaluated and the results are compared in diverse conditions.

This letter provides a generalization of the well-known Wald test. The proposed generalized Wald test (GWT) is a Separating Function Estimation Test (SFET) which is a type of detector recently introduced for a wide class of composite problems. The test statistics of an SFET is an estimate of a real-valued Separating Function (SF). It is already proved that a Minimum Variance Unbiased Estimator of any SF leads to the optimal Uniformly Most Powerful unbiased detector. In many practical cases, such an optimal detector does not exist; hence, suboptimal ones are used, instead. Selecting an SF with a guaranteed performance is still an open problem which is investigated in this letter. First, we derive a lower bound for the detection probability of the SFET in terms of corresponding SF and the Fisher Information Matrix. Then we optimize the proposed bound with respect to the SF. The solution of the optimization problem leads to a generalization of the Wald test which is asymptotically optimal and reduces to the Wald detector in some special cases. Simulation results show the superiority of the proposed GWT over its counterparts, namely, the Wald test and GLR detector in some examples.

This paper investigates simultaneous wireless information and power transfer (SWIPT) in energy harvesting (EH) relay systems. Unlike the existing SWIPT schemes requiring the instantaneous channel state information (CSI) for coherent information delivery, we propose a noncoherent SWIPT framework for decode-and-forward (DF) relay systems bypassing the need for the instantaneous CSI and consequently saving energy in the network. The proposed SWIPT framework embraces both the power-splitting noncoherent DF (PS-NcDF) and timeswitching noncoherent DF (TS-NcDF) in a unified form, and supports arbitrary M-ary noncoherent frequency-shift keying (FSK) and differential phase-shift keying (DPSK). The exact (noncoherent) maximum-likelihood detectors (MLDs) for PS-NcDF and TS-NcDF are derived in a unified form, which involves integral evaluations yet serves as the optimum performance benchmark for noncoherent SWIPT. To reduce the computational cost of the exact MLDs, we also propose closed-form approximate MLDs achieving near-optimum performance, thus serving as a practical solution for noncoherent SWIPT. Numerical results demonstrate a performance tradeoff between the first and second hops through the adjustment of time switching or power splitting parameters, whose optimal values minimizing the symbol-error rate (SER) are strictly between 0 and 1. We demonstrate that M-FSK results in a significant energy saving over M-DPSK for M ≥ 8; thus M-FSK may be more suitable for EH relay systems.

In energy harvesting (EH) relay networks, the coherent communication requires accurate estimation/tracking of the instantaneous channel state information (CSI) which consumes extra power. As a remedy, we propose two noncoherent EH relaying protocols based on the amplify-and-forward (AF) relaying, namely, power splitting noncoherent AF (PS-NcAF) and time switching noncoherent AF (TS-NcAF), which do not require any instantaneous CSI. We develop a noncoherent framework of simultaneous wireless information and power transfer (SWIPT), embracing PS-NcAF and TS-NcAF in a unified form. For arbitraryM-ary noncoherent frequency-shift keying (FSK) and differential phase-shift keying (DPSK), we derive maximum-likelihood detectors (MLDs) for PS-NcAF and TS-NcAF in a unified form, which involves integral evaluations yet serves as the optimum performance benchmark. To avoid expensive integral computations, we propose a closed-form detector using the Gauss-Legendre approximation, which achieves almost identical performance as the MLD but at substantially lower complexity. These EH-based noncoherent detectors achieve full diversity in Rayleigh fading. Numerical results demonstrate that our proposed PS-NcAF and TS-NcAF may outperform the conventional grid-powered relay system under the same total power constraint. Various insights which are useful for the design of practical SWIPT relaying systems are obtained. Interestingly, PS-NcAF outperforms TS-NcAF in the single-relay case, whereas TS-NcAF outperforms PS-NcAF in the multi-relay case.

In this paper we propose an algorithm with reduced complexity for the sphere detection (SD) which is used in multiple input multiple output (MIMO) detection algorithms without any performance degradation. The trade-off between the complexity and the bit error rate is a main challenge in wireless MIMO systems. The maximum likelihood (ML) detector considered as the optimum detector in the literatures. Since the complexity of the naive ML detectors is significantly high, the SD algorithms are proposed to lower the complexity. In this paper, we use the result of the geometrical decoder (GD) proposed in [8] which performs as the ML detector and has lower complexity than SD algorithm. We propose a method to further reduce the complexity of this SD algorithm. We show that the complexity is further reduced by almost 60%, i.e., the number of nodes visited by the proposed SD method is in average 60% less than that of the original one.

We introduce a nonparametric approach to multiscale segmentation of images using a hierarchical matrix analysis framework called diffusion wavelets. This approach benefits from the advantages of both graph theory and wavelet transform. Till now a broad range of multiscale transforms like wavelets (and other x-lets) have been introduced for image segmentation task. The graph theoretic formulation of grouping is also well-known to deal with this problem. The combination of multiscale transforms and graph based partitioning results in a scale-spectral method exploring through different scales of the image, over a great deal of spectral methods in graph partitioning. The method constructs multiscale basis functions and a series of dilation and orthogonalizations build a hierarchy, automatically. At each level, a set of basis functions is built by applying dyadic powers of a diffusion operator on the bases at the lower level. Two approaches are proposed for multiscale segmentation of images using diffusion wavelets. The first method is based on extended bases functions at each level and designing a competition between the bases value for partitioning. The second approach is defining a new distance for each level and clustering based on such distances.

Microarray technology has become a power tool in the field of bioinformatics. It is used to measure gene expression levels and similar to any other image capturing processes is prone to noise. There are different kinds of noise, during preparation, hybridization and scanning in microarray images which usually are modeled by Gaussian noise. Since introduction of wavelets in 1970s, many more forms and extensions of this transform have been developed and used, such as stationary wavelet transform (SWT), complex wavelet transform (CWT), curvelet transform (CURV) and contourlet transform (CNT). By developing of more sparse transforms, it is important to have a perspective of how efficient the transforms are in different applications, such as microarray image analysis. In this paper, we compare the efficiency of common sparse transforms including ordinary discrete wavelet transform (DWT), SWT, CWT, CURV, CNT, Contourlet-SD decomposition, steerable pyramid (STP) and shearlet transform (SHR) for microarray image denoising. Therefore after converting microarray image into x-let transform, BayesShrink method, soft and hard thresholding are used to perform denoising of these images. Both local and general thresholds are calculated for each subband in order to evaluate the effect of incorporating intrascale dependency on top of sparsity property in statistical modeling of x-let's coefficients. Our simulation results show that CWT and SHR outperforms the others when using global thresholding and SWT is the preferred transform when using local thresholding. Although STP and SHR have better performance for some criteria like structural similarity (SSIM) index, but CWT is faster.

In this paper, we introduce a method of hyperspectral unmixing in the linear mixing model with the given library of the constituent materials. The proposed algorithm employs an arctan function to approximate the l0norm in the minimization problem. This approximation makes the objective function smooth, facilitates the convergence and results in reduced reconstruction errors. We evaluate the proposed method and compare it with other methods via simulation. This reveals that the proposed method outperforms the state-of-the-art methods and results in higher reconstruction signal-to-noise-ratio.

The collaborative Spectrum Sensing (SS) to detect random signals contaminated by additive Gaussian noise is studied. A novel soft decision rule based on the Lp-norm of square root of the Secondary Users (SUs) energies is proposed. The proposed scheme considers the bandwidth limitation for sharing sensing information between the SUs. Whereas the existing literature on Lp-norm detectors have assumed independent observations of the SUs, they would be highly correlated when Primary User (PU) is active. Therefore, we consider the correlated observations of the distributed SUs for the both cases of equal and unequal received Signal-to-Noise Ratios (SNRs). We derive the optimal Lp-norm vector parameter based on minimization of the overall error probability while meeting the spectral efficiency and sensing performance requirements. To this end, we use modified deflection coefficient metric to simplify optimization problem. Furthermore, in order to evaluate the performance of the proposed detector, the closed form expressions for detection and false alarm probabilities are computed analytically. The provided closed-form analytical results in addition to simulation results show that the proposed detector outperforms significantly the traditional energy combining detector.

Vehicle-to-vehicle (V2V) communications have recently received growing attentions due to the fact that it enables dynamic wireless data exchange between nearby vehicles, offering the opportunity for safety improvements and information sharing. In this paper, we investigate the performance analysis of a two-way amplify-and-forward (AF) multi-relay cooperative V2V communication networks with MPSK modulation, for two cases where the relays are vehicles or they are access-points of the road. In the mobile-to-mobile communication system considered in this paper, the communication channel is modeled as double-Rayleigh fading channel. We derive the exact symbol error probability (SEP) expressions under the two-phase and three-phase scenarios followed by their upper bound expressions. Then we examine the effect of the relays' position with respect to the source terminals, on their SEP and lastly, the maximum achievable diversity order under each scenario is studied.

A cooperative spectrum‐sensing problem has been considered here, in which a network of secondary users (SUs) assists a fusion centre (FC) in detecting the presence of a primary user (PU). Assuming communication links with unlimited capacity of the SUs and FC and known channel gains and noise variances, the optimal Neyman–Pearson detector is derived. Assuming limited capacity between the SUs and FC and unknown channel gains and noise variances, three different spectrum‐sensing protocols have been studied; namely, amplify‐and‐forward (AF), compress‐and‐forward (CF) and detect‐and‐forward (DF), where each SU transmits an amplified or compressed version of its observed signal, or its local binary decision to the FC, respectively. The Edgeworth expansion is used to obtain novel expressions for the performance of these detectors. The theoretical analysis and numerical results show that the CF and OR detectors outperform the other proposed detectors. In addition, the simulation results show that the performance of the coded protocols (CF and DF) improves as the number of samples increases or as the noise variance at the SUs decreases, whereas such a behaviour cannot be guaranteed in the uncoded AF protocol.

The authors consider a half‐duplex asynchronous code division multiple access cooperative network with N source–destination (SD) pairs and a number of relay nodes where the nodes of all pairs have to exchange data in two hops via assistance of one of the available relays. In this study, they minimise the total transmit power and derive the closed‐form solution for choosing the best relay, the best relay gain and the transmit powers of all sources where some predefined signal‐to‐interference plus noise‐ratios (SINRs) are guaranteed. Interestingly, the feasibility condition of the problem depends only on the required SINRs, the number of SD pairs and the maximum cross‐correlation of users’ codes. They suggest two control procedures for admitting or dropping of users to the network to satisfy the feasibility condition. For a reciprocal environment, the best relay and its gain are proved to remain unchanged for reversing the communication directions. In addition, the authors’ power control algorithm can be directly applied to the case of two‐hop two‐way relaying. Computer simulations are used to demonstrate the system performance.

The authors consider a linear relaying communication system using multiple antennas at a source, relay and destination. They maximise the end‐to‐end signal‐to‐noise‐ratio (SNR) to find the relay transformation (RT) and the source transmit covariance (STC) matrices. For any given STC matrix, to maximise the SNR, they prove that the relay has to assign all its power in the direction of the dominant eigenmodes of the source–relay and the relay–destination channels. They also find the joint optimal rank‐one matrices for the STC and RT. They prove that this solution also maximises the source–destination mutual information among all rank‐one matrices. Furthermore, they find the optimal power budgets allocated to the source and relay that maximise the SNR under a constraint on the total transmit power of the system. This is a practical solution as only three positive quantities need to be communicated among the nodes to calculate these optimal power budgets. Interestingly, the authors computer simulations for multiple users reveal that the sum‐rate significantly increases if users selfishly maximise their own SNRs using the proposed method, instead of maximising their own capacity. Thus, they conclude that the SNR‐maximisation is spectrally more efficient as it consumes only the best subspaces and leaves the other subspaces free.

A relay communication system where its nodes are equipped with multiple antennas is considered. The relay applies a transformation matrix on its received signal and forwards it to the destination. The authors show that, given a limited relay transmit power, the transformation matrix which maximises the signal‐to‐noise power ratio (SNR) is almost surely a rank‐one matrix. Such a singular transformation matrix prevents accurate channel estimation. As a remedy, they impose a constraint on the condition number (CN) of the relay transformation matrix. They prove that the optimal relay matrix under this new constraint has up to two distinct singular values. Thus, the relay transmits some power in all directions, and this small energy allows better channel estimation. The author's simulation results show that imposing the CN constraint on the relay matrix results in some small loss in the SNR which is quantified in this study; however, the optimal relay matrix enables the receivers to track the channel state information.

In this paper, we study the MIMO signal detection problem using widely separated antennas in Gaussian interference. The interference is assumed to be colored with unknown N × N covariance matrix. We derive the Uniformly Most Powerful Invariant (UMPI) test for this detection problem as the upper performance bound for invariant tests. Also the Separating Function Estimation Test (SFET) is derived for this problem using the Signal and Scatter to Interference Ratio (SIR). Then, based on the eigenvalues expansion of SIR, we propose a set of orthogonal signals in transmitters which maximizes the detection probability of UMPI and SFET. Simulation results show that 1) the performance of our proposed detector is close to the UMPI bound and 2) the performance of the optimal invariant bound improves when the transmitters use the proposed set of signals instead of the orthogonal complex exponential signals.

The authors consider a relay communication system, where all nodes are equipped with multi‐input multi‐output antennas, and there is a direct path/channel between the source and the destination. Assuming a linear non‐regenerative relaying, the relay matrix is designed by maximising the signal‐to‐noise ratio of the system. The authors derive the optimum relaying matrix for different power constraints in the relay, including the fixed relay power constraint and constraint on the maximum transmitting power from the relay. Under the constraint of fixed total transmit power, they derive the optimum power budgets of the relay and source that only depend on four positive quantities. They prove that there always exists a rank‐one relaying matrix, which transmits signal in one‐dimensional subspace and leaves the other subspaces clean. In addition, when the quality of the source–destination (SD) channel is poor, this matrix is the best rank‐one relaying transform that maximises mutual information between the source and the destination. Finally, they conclude that the relaying is beneficial only if the link quality for the source–relay is twice better than that of the SD, where the link quality is proportional to the ratio of the channel power gain to the received noise variance.

The demand for wireless access continues to increase rapidly in both military and civilian communities. The modern internet and modern personal-area devices have made billions of users around the world accustomed to data-hungry applications such as videos. This has an inevitable effect on the users' desire for the same through wireless media. The articles in this special issue focus on new theories and methods for advanced wireless relay technologies.

We develop closed-form generalized likelihood ratio test (GLRT) sequence detectors for multi-relay amplify-and-forward (AF) cooperative networks employing M-ary differential phase-shift keying (M-DPSK) and noncoherent M-ary frequency-shift keying (M-FSK). The proposed detectors achieve robust performance in a wide range of fading environments where prior knowledge of channels, signal powers, noise variances, and relay functionalities is unavailable to the destination. A comprehensive error probability performance analysis is carried out in Rayleigh fading. Specifically, we derive unified pairwise error probability (PEP) expressions for both detectors in a dual-hop network, which are valid for arbitrary modulation order M and arbitrary sequence length Ns. We also derive unified bit-error probability (BEP) expressions for both detectors employing binary signalings in various networks for Ns= 1. It is further shown that both detectors achieve near full diversity orders. Finally, the superiorities of the proposed detectors over the state-of-the-art noncoherent detectors are justified through extensive comparisons in practical scenarios. For example, in a multi-relay network where the relays are uniformly distributed between the source and destination, the proposed detector for noncoherent FSK with Ns= 1 outperforms the well-known maximum energy selector by almost 15 dB in high signal-to-noise ratios.

In this paper, we study distributed energy detectors for spectrum sensing in cognitive radio networks. We treat the primary signal as unknown and deterministic with a known power and the channels as random with a Nakagami-Lognormal mixture distribution. In other words, the channel gains are modeled as product of two terms corresponding to the small and large-scale fadings. The small-scale terms are all Nakagami and independent for all sensors. Whereas for large-scale fading, sensors in each cluster are assumed to have identical Lognormal terms. We also assume that different clusters have independent large-scale fading. Assuming that the total energies from clusters are received, we investigate four distributed detection strategies: 1) Equal Gain Combining detector (EGCD), 2) Neyman-Pearson detector (NPD) or Likelihood Ratio detector (LRD) for known channel gains, 3) Generalized Likelihood Ratio detector (GLRD) for unknown gains, and 4) Average Likelihood Ratio detector (ALRD) given a priori distribution of channel gains. The simulation results show that the effects of both small and large-scale fadings on sensing performance should be considered simultaneously and confirm that a priori statistical information about large and small-scale fading used in ALRD result in performance improvement compared to EGCD and GLRD.

We propose a reactive opportunistic relaying (OR) scheme for time-division broadcast (R-TDBC) in a bidirectional cooperative network. In the proposed R-TDBC, a subset of relays that reliably decode both signals from the sources is determined reactively after two source-transmissions; then a single relay among the determined set is selected by maximizing the achievable rate of the broadcast phase. A somewhat surprising result is that the proposed R-TDBC achieves identical outage performance as its proactive counterpart in bidirectional cooperative networks with the direct link. We then derive the exact outage probability, tight closed-form bounds, and the diversity-multiplexing tradeoff (DMT) for the R-TDBC.

In this paper, we investigate the effect of applying an exponential window on the limiting spectral distribution (l.s.d.) of the exponentially windowed sample covariance matrix (SCM) of complex array data. We use recent advances in random matrix theory which describe the distribution of eigenvalues of the doubly correlated Wishart matrices. We derive an explicit expression for the l.s.d. of the noise-only data. Simulations are performed to support our theoretical claims.

We study relay selection (RS) for time-division broadcast (TDBC) in a bidirectional network consisting of two different end-sources and multiple relays. By modifying the well-known proactive RS scheme valid only for unidirectional cooperative networks, we propose a proactive RS with TDBC (P-TDBC) for bidirectional cooperative networks. In the P-TDBC scheme, a single best relay is selected proactively before two consecutive source-transmissions by maximizing the bottleneck of the end-to-end maximal achievable rates in both directions. We then derive the exact outage probability of P-TDBC in closed-form for Es≤ Er, and in a single-integral form for Es>; Er, where Esand Erdenote the transmission powers at each end-source and the selected relay, respectively. Moreover, we derive very tight closed-form upper and lower bounds on the outage probability for Es>; Er. Finally, we study the diversity-multiplexing tradeoff (DMT) and show that the P-TDBC scheme achieves full diversity.

The authors assume a linear equidistance antenna array as the receiver for a fixed frame-length multiple-access system, which employs variable-gain receiver power and repetition encoding. They propose a robust maximum a posteriori probability (MAP) blind bit-rate detector (BBRD). This detector considers the rate detection (RD) as a multi-hypothesis test and maximises the likelihood functions (LFs) to find the true bit-rate, whereas the complex amplitude of the received signal, the noise variance and the direction of arrival are unknown parameters. First, assuming that the location parameter is known and the information sequence are independent and uniformly distributed random variables, the authors propose a hybrid likelihood ratio test (HLRT). The proposed HLRT requires to solve a set of non-linear equations that have no closed-form solution. Thus, an iterative numerical algorithm is proposed. In addition, a quasi-HLR detector which has a significantly lower computational complexity is also proposed. In the case of unknown location parameter, the authors develop two quasi-HLR methods. They use fast Fourier transform and search to estimate the unknown location. In Q-HLRT-Method-I, the non-linear equation similar to the one in known location parameter is iteratively solved. In Q-HLRT-Method-II, a low complexity solution is proposed. Simulation examples evaluate and compare the performances of the proposed BBRDs.

This study presents a method based on minimum description length criterion to enumerate the incident waves impinging on a large array using a relatively small number of samples. The proposed scheme exploits the statistical properties of eigenvalues of the sample covariance matrix (SCM) of Gaussian processes. The authors use a number of moments of noise eigenvalues of the SCM in order to separate noise and signal subspaces more accurately. In particular, the authors assume a Marcenko–Pastur probability density function (pdf) for the eigenvalues of SCM associated with the noise subspace. We also use an enhanced noise variance estimator to reduce the bias leakage between the subspaces. Numerical simulations demonstrate that the proposed method estimates the true number of signals for large arrays and a relatively small number of snapshots. In particular, the proposed method requires less number of samples to achieve the same correct enumeration probability compared to the state-of-the-art methods. The authors evaluated the assumed pdf in order to justify the limitation and the behaviour of the proposed method for small number of snapshots and array sizes.

The 46 papers focusing on the theme of "Theories and Methods for Advanced Wireless Relays" have been divided into two groups to be published in two separate issues. This first issue includes 23 papers on relay performance bound, MIMO relay beamforming, relay channel estimation, two-way and shared relays, full-duplex relays, and security for relay networks. The second issue includes papers on coding for relay networks, medium access control for relays, implementation ans system performance studies.

The performance of Orthogonal Frequency Division Multiplexing (OFDM) systems is highly impacted by clipping distortions caused by non-linear amplifiers. One approach known as Tone Reservation (TR) method is to allocate/use a small number of sub-carriers to generate more suited signals and reduce the impact of these non-linear distortions. Traditionally, existing TR algorithms attempt to minimize the Peak-to-Average Power Ratio (PAPR). In this paper, we show that maximization of the Signal-to-Distortion Ratio (SDR) is a better criterion which achieves a better symbol error rate performance. Our results reveal that the proposed approach outperforms in terms of error probability rate for the same transmit power and same order of computational cost. Interestingly, the PAPR value for the proposed algorithm is not better than the state of the art algorithm in which directly optimizes the PAPR.

In this paper we investigate the local probability density function (pdf) of natural signals in sparse domains. The statistical properties of natural signals are characterized more accurately in the sparse domains because the sparse domain coefficients (SDCs) have heavy-tailed distribution and have reduced correlation with adjacent coefficients. Our experiments show that a conditionally (given locally estimated variance and shape) independent Bessel K-form (BKF) pdf locally fits the sparse domain's coefficients of natural signals, accurately. To justify this observation, we also investigate the pdf of the locally estimated variance and suggest a Gamma pdf for the locally estimated variance. Since commonly used sparse transformations are orthonormal, the pdf of the sparse domain coefficients must converge to Gaussian distribution by virtue of central limit theorem assuming that natural signals are locally wide sense stationary for small window sizes. Interestingly, we observe that the pdf of the normalized data (on the locally estimated variance) exhibit a Gaussian pdf, which justifies why the BKF pdf is an appropriate fit.

In this letter, we develop a computationally efficient algorithm for the Maximum Likelihood (ML) sequences estimation (MLSE) of an M-ary Phase Shift keying (M-PSK) signal transmitted over a frequency non-selective slow fading channel with an unknown complex amplitude and an unknown variance additive white Gaussian noise. The proposed algorithm also provides the ML estimates of the complex amplitude and the noise variance that are critical in signal activity detection and demodulation in the modern cognitive communication receivers. We prove the optimality of the proposed algorithm and compare its performance via simulation with a recent suboptimal algorithm.

In this letter, we consider a power allocation problem for digital subscriber line (DSL) systems. The goal of this problem is to minimize the total transmit power under some constraints on minimum data-rate and maximum transmit power for each modem where we take into account various sources of interference. We convert our problem into an auxiliary geometric programming (GP) which gives the optimum solution for transmit powers in a neighborhood of a given feasible point. Then, we use an iterative scheme for obtaining the solution to our original problem by exploiting this auxiliary GP problem. Numerical examples show that the proposed method outperforms the existing schemes.

We consider two nodes equipped with multiple antennas that intend to communicate i.e. both of which transmit and receive data. We model the responses of the communication channels between these nodes as linear and reciprocal (time invariant or with very slow time variations). In practice, we exploit the closed loop conversation between these nodes and present an efficient algorithm allowing to adaptively identify the Best Singular Mode (BSM) of the channel. We consider two scenarios. In the first scenario, the initial communication link is established over the BSM assuming that the exchanged data is partially known at both nodes. This scenario is suitable for channel training. In the second scenario, the BSM is adaptively updated while the real unknown data is exchanged between the nodes i.e. no capacity is wasted for channel identification. The proposed adaptive algorithm is robust to noise as the involved step-size allows a trade-off to reduce the impact of the additive noise at the expense of some estimation delay. Our computer simulations show that the proposed algorithm works efficiently in both modes of operations (training mode and simultaneous training/data transmission mode) for both static and slow fading MIMO channels and for both white and colored noises.

In this paper, we consider the problem of spectrum sensing by using multiple antenna in cognitive radios when the noise and the primary user signal are assumed as independent complex zero-mean Gaussian random signals. The optimal multiple antenna spectrum sensing detector needs to know the channel gains, noise variance, and primary user signal variance. In practice some or all of these parameters may be unknown, so we derive the generalized likelihood ratio (GLR) detectors under these circumstances. The proposed GLR detector, in which all the parameters are unknown, is a blind and invariant detector with a low computational complexity. We also analytically compute the missed detection and false alarm probabilities for the proposed GLR detectors. The simulation results provide the available traded-off in using multiple antenna techniques for spectrum sensing and illustrates the robustness of the proposed GLR detectors compared to the traditional energy detector when there is some uncertainty in the given noise variance.

The authors propose invariant tests for the detection of a complex signal with unknown constant amplitude and unknown phase variation in additive white Gaussian noise (AWGN). The authors show that in this problem, the uniformly most powerful invariant (UMPI) detector does exist only if the number of samples N is two. For more than two samples N≥3, the authors derive the most powerful invariant (MPI) detector in known signal-to-noise ratio (SNR) and use its performance as the upper bound benchmark for any invariant test. In addition, the authors derive the generalised likelihood ratio (GLR) detector and evaluate its performance against the MPI performance bound. This detector is very simple and represents the ratio of the L1-norm to the L2-norm of the data. Simulation results illustrate the close performances of the two detectors even at low SNRs, whereas in contrast to the MPI test the SNR is not required in the proposed GLR test. In order to understand why the knowledge of SNR is not so important in this detection problem, the authors also derive the GLR test for the case of known SNR. Interestingly, the resulting GLR detector (derived for the case of known SNR) turns out to be equivalent to the one derived for unknown SNR, i.e. a knowledge of the SNR is not used in any of the GLR tests. This reveals why the knowledge of the SNR is not so useful in this detection problem.

We investigate the relay selection problem in a network where users are able to collaborate with each other; decode and forward the messages of each other along with their own messages to the destination. We study the performance obtained from collaboration in terms of (1) increasing the achievable rate, (2) saving the transmit energy, and (3) reducing the resource (time-bandwidth) requirement. To ensure fairness, we assume that the transmit energy to the rate ratio is fixed for all users. We allocate resource optimally for the proposed collaborative protocol (CP) and compare the result with the non-collaborative protocol (NCP) where users transmits their messages directly to the destination. The collaboration gain allows us (1) to decide whether to collaborate or not and (2) to select one relay among the possible relay users. We show that a considerable gain can be obtained if the direct source-destination channel gain is significantly smaller than those of alternative links. We demonstrate that a rate and energy improvement of up to (1+n√(k/k+1)) can be obtained, wherekis the environment path loss exponent and k is the ratio of the rates of involved users. We also show that the collaboration is only beneficial for the middle range rate ratio.

In this paper, we divide a wide frequency range into multiple subbands and in each subband detect whether in a primary user (PU) is active or not. We assume that PU signal at each subband and the additive noise are white zero-mean independent Gaussian random processes with unknown variances. We also assume that at least a minimum given number of subbands is vacant of PU signal and propose an invariant generalized likelihood ratio (GLR) detector. The concept of the grouping of subbands allows faster spectrum sensing of a subset of subbands which may be occupied by a specific PU. Also, we evaluate trade-offs involved in the proposed algorithms by simulation.

We consider an ad hoc network consisting of d source-destination pairs and R relaying nodes. Each source wishes to transmit its data to its corresponding destination through the relay network. Each relay in the network transmits a properly scaled version of its received signal thereby cooperating with other relays to deliver each source's data to the corresponding destination. Assuming a minimal cooperation among the relaying nodes, we design a distributed beamformer such that the total relay transmit power dissipated by all relays is minimized while, at the same time, the quality of services at all destinations are guaranteed to be above certain pre-defined thresholds. We show that using a semi-definite relaxation approach, the power minimization problem can be turned into a semi-definite programming (SDP) optimization, and therefore, it can be solved efficiently using interior point methods. Our results show that the distributed relay multiplexing is possible and may be beneficial depending on the channel conditions.

We propose a simple multiple description (MD) video coding technique motivated by human visual perception. This method employs two simple parameters to characterize the smoothness and edge features of DCT blocks and measure their perceptual tolerance against visual distortion. We duplicate the key components of compressed video and split the remaining according to the calculated perceptual tolerance parameter. Simulation results reveal that our simple MD video coding method achieves superior performance compared to other similar techniques which lack to consider the perceptual distortion in the design problem.

We derive expressions for the achievable rate region of a collaborative coding scheme in a two-transmitter, two-receiver pairwise collaborative network (PCN) where one transmitter and receiver pair, namely relay pair, assists the other pair, namely the source pair, by partially decoding and forwarding the transmitted message to the intended receiver. The relay pair provides such assistance while handling a private message. We assume that users can use the past channel outputs and can transmit and receive at the same time and in the same frequency band. In this collaborative scheme, the transmitter of the source pair splits its information into two independent parts. Ironically, the relay pair employs the decode and forward coding to assist the source pair in delivering a part of its message and re-encodes the decoded message along with private message, which is intended to the receiver of the relay pair, and broadcasts the results. The receiver of the relay pair decodes both messages, retrieves the private message, re-encodes and transmits the decoded massage to the intended destination. We also characterize the achievable rate region for Gaussian PCN. Finally, we provide numerical results to study the rate trade off for the involved pairs. Numerical result shows that the collaboration offers gain when the channel gain between the users of the relay pair are strong. It also shows that if the channel conditions between transmitters or between the receivers of the relay and source pairs are poor, such a collaboration is not beneficial.

The impact of non-straight movements of a mobile station (MS), for example rotation of the MS, on the statistics of a wireless propagation channel needs to be studied in the modern literature of wireless channel modelling. The authors formulate the effects of MS rotation on a cross-correlation model for a multiple-input multiple-output Rayleigh fading channel in a two-dimensional (2D) multipath propagation medium. The authors consider communication over a non-isotropic wave propagation environment employing directional antenna propagation patterns (APPs) at both transmitter and receiver arrays. The cross-correlation function (CCF) is computed when the MS rotates with a constant angular velocity and moves with a constant linear speed on the 2D plane. It is shown that the angular movement of the MS results in a cyclostationary channel process. In such a situation, the authors analyse the derived CCF using the continuous-time Fourier series in terms of the MS angular velocity, the parameters of the propagation environment and the employed antennas. The numerical evaluation shows that the energy dispersion in different frequencies is significantly influenced by the type of the directional APP, the distribution of scatterers and the angular Doppler shift. Furthermore, the authors establish an approximate relationship between the coherence-time and the MS angular velocity using a numerical curve fitting.

The spectrum sensing of a wideband frequency range is studied by dividing it into multiple subbands. It is assumed that in each subband either a primary user (PU) is active or absent in a additive white Gaussian noise environment with an unknown variance. It is also assumed that at least a minimum given number of subbands are vacant of PUs. In this multiple interrelated hypothesis testing problem, the noise variance is estimated and a generalised likelihood ratio detector is proposed to identify possible spectrum holes at a secondary user (SU). Provided that it is known that a specific PU can occupy a subset of subbands simultaneously, a grouping algorithm which allows faster spectrum sensing is proposed. The collaboration of multiple SUs can also be considered in order to enhance the detection performance. The collaborative algorithms are compared in terms of the required exchange information among SUs in some collaboration methods. The simulation results show that the proposed detector outperforms the energy detector in the presence of noise variance mismatch above 2.3 dB. Some involved trade-offs in the spectrum sensing using the proposed detector are discussed.

In outdoor environments, waves propagate non- isotropically because of non-uniform distribution of scatterers. In addition directional antennas, which are efficient for communication applications, have considerable impact on the response of the communication channel. The impact of non-isotropic propagation and directional antennas is more significant when the system employs multiple antennas. In this paper, we propose a space-time-frequency cross-correlation function (CCF) for multiple-input multiple-output multicarrier channels in a two-dimensional (2D) random scattering medium. The expression of the CCF turns out to be a multiplication of three CCFs. Two of these terms characterize the impact of mobile station and base station, respectively and are linear Bessel expansions, where the coefficients are given by the linear convolution of the Fourier series coefficients (FSC)s of the employed antenna patterns and the FSCs of the probability density function describing 2D non-isotropic environment. The third expression characterizes the impact of the wireless channel. In the stationary case, we derive the expression of the fading channel power spectrum (CPS) in terms of the non-isotropic pdf and antenna patterns. Using the expression of the CCF, we also calculate coherence-bandwidth (CB)/coherence-time (CT) as a function of carrier-frequency/time separations. Our numerical results show a good fit to the available experimental results obtained for the CB/CT of realistic outdoor channels.

There are two types of scatterers in a multipath propagation environment: fixed and non-fixed scatterers. Fixed scatterers like buildings, mountains and tree stems do not move, while non-fixed (moving) scatterers like cars, people and tree leaves move from their initial positions. In addition to different movements of the mobile station (MS) that cause different kinds of Doppler fading, movements of non-fixed scatterers are also anticipated to generate other kinds of fading/correlation. This fading changes depend on the type of the propagation environment and the type of movements of local surrounding scatterers. In this paper we characterize these effects in terms of temporal correlations. In this study we assume constant speed for the MS and random displacements for scatterers based on stationary-increment Wiener vector process. As it will be verified, these random movements have direct effect on the channel process power spectral density (PSD).

In this paper, we present a new image denoising algorithm. We assume a mixture of bivariate circular symmetric Laplacian probability density functions (pdfs) where for each wavelet coefficients may have different local parameter. This pdf characterizes simultaneously 1) the heavy-tailed nature, 2) the interscale dependencies of the wavelet coefficients and also 3) the empirically observed correlation between the coefficient amplitudes. We employ this local bivariate mixture model to derive a Bayesian image denoising technique. This proposed pdf, potentially can fits better the statistical properties of the wavelet coefficients than several other existing models. Our simulation results reveal that the proposed denoising method is among the best reported in the literature. This is justified since the accuracy of the employed distribution for noise-free data determines the denoising performance.

In this paper we propose a new model for multiple-input multiple-output (MIMO) multicarrier channels in a 3D scattering medium. The proposed model describes the cross-correlation function (CCF) between two sub-channels of an outdoor MIMO channel employing directional antennas in the presence of non-isotropic wave propagation in the 3D space. The derived CCF consists of some correlation terms. Each correlation term is in the form of a linear expansion of averaged Bessel functions of the first kind with different orders. In practice, each correlation term has a limited number of components. Our evaluations of the CCF show the impact of different parameters of the propagation environment and the employed antennas. Our numerical results appear as a good match to the available reported experimental data of some 3D outdoor propagation media.

Mapping the end to end QoS requirements into link QoS requirements is an important step for resource allocation of connection oriented services. The problem of the QoS partitioning has been addressed in literature and proved to be NP complete. Different algorithms are proposed to solve the problem of single end-to-end QoS metric. However, these algorithms are near optimal or heuristic algorithms and solve the QoS partitioning problem for single QoS metric. In this paper, we propose a novel optimal partitioning algorithm which is capable of partitioning the end to end QoS requirement for multiple QoS metrics, additive and multiplicative, simultaneously. Extensive simulation verified the effectiveness of the algorithm compared to two QoS partitioning algorithms. The results show that the proposed algorithm outperforms the other two algorithms for loose and stringent QoS requirements and over different path lengths.

The performance of various estimators, such as maximum a posteriori (MAP) is strongly dependent on correctness of the proposed model for noise-free data distribution. Therefore, the selection of a proper model for distribution of wavelet coefficients is very important in the wavelet based image denoising. This paper presents a new image denoising algorithm based on the modeling of wavelet coefficients in each subband with a mixture of Laplace probability density functions (pdfs) that uses local parameters for the mixture model. The mixture model is able to capture the heavy-tailed nature of wavelet coefficients and the local parameters can model the empirically observed correlation between the coefficient amplitudes. Therefore, by using this relatively new model, we are able to model the statistical properties of wavelet coefficients. Within this framework, we describe a novel method for image denoising based on designing a MAP estimator, which relies on the mixture distributions with high local correlation. The simulation results show that our proposed technique achieves better performance than several published methods both visually and in terms of peak signal-to-noise ratio (PSNR).

Recently, it has been shown that algorithms exploiting dependencies between coefficients for modeling probability density function (pdf) of wavelet coefficients, could achieve better results for image denoising in wavelet domain compared with the ones based on the independence assumption. In this context, we design a bivariate maximum a posteriori (MAP) estimator which relies on a mixture of bivariate Laplacian models. This model not only is bivariate but also is mixture and therefore, using this new statistical model, we are able to better capture heavy-tailed natures of the data as well as the interscale dependencies of wavelet coefficients. The simulation results show that our proposed technique achieves better performance than several published methods both visually and in terms of peak signal-to-noise ratio (PSNR)

Several models have been proposed for the simulation of Rayleigh fading channels. However, existing simulators lack to properly consider the correlation between the subchannels of an OFDM system. We use a recently developed cross-correlation function that describes both temporal and frequency correlation in order to generate channel parameters. This correlation function is decomposable into multiplication of two correlation functions. The first term characterizes only the temporal correlation and the other characterizes the correlation between subchannels. Using this property, the proposed simulator is implemented in cascade of two steps. In the first step, we propose an improved IFFT method for generation of multiple independent temporally correlated complex Gaussian processes following the given temporal correlation. We then transform these processes into a vector random processes by a transformation which is obtained by factorization of the frequency-correlation matrix. Our results reveal that the proposed technique accurately generates the desired statistical properties. This method is efficient in terms of computation complexity and runtime cost.

Considering a real signal as the sum of a number of sinusoidal signals in the presence of additive noise, maximum windowed likelihood (MWL) criterion is introduced and applied to construct an adaptive algorithm in order to estimate the amplitude and frequency of these components. The amplitudes, phases and frequencies are assumed to be slowly time varying. Employing MWL an adaptive algorithm is obtained in two steps. First, assuming some initial values for the frequency of each component, a closed form is derived to estimate the amplitudes. Then, the gradient of MWL is used to adaptively track the frequencies, using the latter values of amplitudes. The proposed algorithm has a parallel structure in which each branch estimates parameters of one of the components. The proposed multicomponent phase locked loop (MPLL) algorithm is implemented employing low complexity blocks. It is adjustable to be used in different conditions. The mean squared error of the algorithm is studied to analyze the effect of the window length and type and the step size. Simulations have been conducted to illustrate the efficiency and the performance of the algorithm in different conditions including: the effect of the initialization, the frequency resolution, for chirp components, for components during frequency crossover and for speech signals. Simulations illustrate that the method efficiently tracks slowly time-varying components of the signals such as voiced speech segments.

We propose computationally inexpensive and efficient solutions for signal activity detection of phase-shift keying (PSK) signals in additive white Gaussian noise. We consider the complex amplitude of the signal as well as the information sequence as the unknown parameters. In addition, the noise variance is assumed unknown. We derive the generalized likelihood ratio test (GLRT) and suggest a computationally efficient implementation thereof. Furthermore, we develop a new inexpensive detector for binary PSK signals, which we will refer to as the generalized energy detector. To evaluate the performance of these detectors, we attempt to derive a uniformly most powerful invariant test (UMPI) as an optimal detector. It turns out that the UMPI test exists only if the signal-to-noise ratio is known. We use this UMPI test in order to obtain an upper-bound performance for the evaluation of invariant detectors, such as the above-mentioned GLRT. Simulation results illustrate and compare the performance and the efficiency of the proposed signal activity detectors.

We propose computationally inexpensive and efficient solutions for signal activity detection of phase-shift keying (PSK) signals in additive white Gaussian noise. We consider the complex amplitude of the signal, as well as the information sequence, as the unknown parameters. In addition, the noise variance is assumed unknown. We derive the generalized likelihood ratio test (GLRT) and suggest a computationally efficient implementation thereof. Furthermore, we develop a new inexpensive detector for binary PSK signals, which we will refer to as the generalized energy detector. To evaluate the performance of these detectors, we attempt to derive a uniformly most powerful invariant (UMPI) test as an optimal detector. It turns out that the UMPI test exists only if the signal-to-noise ratio is known. We use this UMPI test in order to obtain an upper-bound performance for the evaluation of invariant detectors, such as the GLRT. Simulation results illustrate and compare the performance and the efficiency of the proposed signal activity detectors

In this paper, we characterize the cross-correlation function (CCF) between the time-frequency transfer functions (TF)s of two sub-channels of a multiple-input multiple-output (MIMO) wireless fading channel. The TF of each sub-channel consists of a number of multi-path components caused by propagation of a transmitted signal in random scattering media. The proposed CCF is expressed in terms of several environmental parameters (such as the moment generating function (MGF) of the delay profile (DP) and the pathloss exponent). It is a summation of two terms: the first term is due to the autocorrelation of multi-path components while the second term is due to the cross-correlation of multi-path components. Each term is a product of several correlation functions. Each of these correlation functions represents different dependencies of the wireless channel in terms of time, carrier frequency and the position of the antenna elements around both the transmitter and the receiver site. Interestingly, the last two terms of these functions are (n/2)-order (or n-order) integrations of the MGF of the DP, evaluated at two carrier frequencies (or the difference between carrier frequencies), where n is the pathloss exponent of the environment

Guaranteeing the statistical delay for real time connections is an area of interest for many researchers, consequently, several analytical and measurement based admission control algorithms (MBAC) are proposed. The proposed analytical algorithms provide solutions based on traffic fluid models or service disciplines which do not isolate the different flows at the schedular. The proposed MBAC algorithm can not meet the distinct violation probability requirements of a connection. Thus, in this paper we propose a new MBAC algorithm, which is capable of meeting the distinct violation probability requirement. The algorithm utilizes the weighted fair queueing (WFQ) scheduler that is capable of isolating the traffic flows. The proposed algorithm is different from other MBAC algorithms, since it admits requests based on delay measurement rather than bandwidth measurement as most MBAC algorithms do. This facilitates meeting the distinct violation probability requirement of the connections. The simulation results revealed that the proposed algorithm can admit three times the deterministic algorithm for distinct delay requirements.

Virtual private LAN services (VPLS) is a new layer 2 (L2) virtual private network (VPN), which allows connection of multipoint customers at the same time. Multi-protocol label switching (MPLS) technology is the most popular candidate for implementing VPLS because it supports traffic engineering (TE) functionality over the established label switching paths (LSP). However, MPLS's TE support is provided over point-to-point (P2P) LSPs. The functionality and signaling of point to multipoint (P2MP) LSPs are not supported in existing MPLS architecture. In this paper, we propose a new on line distributed routing algorithm for supporting QoS bandwidth flow guarantees. The algorithm provides TE functionality over the P2MP VPLS connections in the core MPLS networks. The on-line distributed routing algorithm maximizes the number of admitted requests by avoiding the critical links of other source-destinations sets. The critical links are identified based on the number of times the link is used to route the source-destinations set traffic and the load over the links. The algorithm does not assume any a priori knowledge of the network traffic. The proposed algorithm performance is compared to some existing algorithms such as the widest shortest path (WSP) and the Plotkin algorithms. It shows that it outperforms both algorithms by having lower blocking probability.

We introduce a multiple-input multiple-output (MIMO) space-time-frequency wireless channel model for the wave propagation in three-dimensional (3D) space. The cross-correlation function (CCF) between two subchannels of the MIMO communication system is decomposed into some non-negative functions. These functions are expressed in terms of a selection of channel parameters, such as the carrier frequencies, the delay profile, the path-loss exponent, the softness factor, /spl theta/, the non-uniform distribution of directions of arrival and departure, array geometries, and the mobile speed. We introduce a class of distributions for the elevation angle (EA) spreads as a basis such that any arbitrary (isotropic or non-isotropic) EA distribution can be represented by a convex linear combination of this class. The corresponding term of the CCF of the 3D-MIMO model for any EA distribution equals to the same linear combination of the basic CCF terms associated to the class. This 3D-MIMO model formulates the CCF as a function the spacial separation of antennas, time, and carrier frequencies in terms of physical channel parameters such as mobile speed, delay profile and distribution of scavengers around mobile and base stations.

A new, efficient speech enhancement algorithm (SEA) is developed in this paper. In this low-complexity SEA, a noisy speech signal is first decorrelated and then the clean speech components are estimated from the decorrelated noisy speech samples. The distributions of clean speech and noise signals are assumed to be Laplacian and Gaussian, respectively. The clean speech components are estimated either by maximum likelihood (ML) or minimum-mean-square-error (MMSE) estimators. These estimators require some statistical parameters derived from speech and noise. These parameters are adaptively extracted by the ML approach during the active speech or silence intervals, respectively. In addition, a voice activity detector (VAD) that uses the same statistical model is employed to detect whether the speech is active or not. The simulation results show that our SEA approach performs as well as a recent high efficiency SEA that employs the Wiener filter. The computational complexity of this algorithm is very low compared with existing SEAs with low computational complexity.

To improve the convergence speed and reduce the mean-square error (MSE) of the gradient based adaptive algorithms in colored environments, such as acoustic echo cancellation, a pre-filter-bank (PFB) adaptive algorithm is proposed by minimizing a weighted criterion of squared errors in subbands. The optimal solution obtained by minimizing this criterion is the Wiener filter, independent of the weights. However, these weights have a strong impact on the behavior of the algorithm and have relations with the subband step-sizes. In particular, the optimal weights, which are derived for a random walk time-varying plant in this paper, depend on the spectra of the input signal and the additive noise. Without a priori knowledge of the spectra, for faster initial convergence and better tracking performance in nonstationary environments, a simple variable step-size (VS) algorithm is introduced to the PFB algorithm in each subband for adjusting the subband step-sizes. This new multistep-size algorithm, which is called the variable step-size pre-filter-bank (VSP) algorithm, improves significantly over the traditional full-band VS algorithms in colored environments. The more colored the noise and the input signal, the more significant the improvement. The drawback of this algorithm is the increase of the computational complexity. As the filters in the filter bank are commonly narrow-band; the nondecimated outputs of these filters are highly correlated. This correlation permits us to approximate the subband autocorrelation matrices by single rank matrices to reduce the computational complexity of the algorithm. The simplified version has almost the same performance as the original VSP algorithm. Simulations show that the proposed algorithms are more efficient than LMS in terms of tracking capabilities for colored environments.

This paper studies the effect of rotation of a mobile station (MS) on the cross-correlation function (CCF) derived for a microcellular isotropic wave propagation environment with enough number of scatterers. The statistical model characterizes the propagation media when the MS rotates with a constant angular velocity around an axis perpendicular to the azimuthal plane, and moves with a constant velocity on the azimuthal plane in an arbitrary direction. The moving MS, with a constant speed, produces the conventional linear Doppler while the rotating MS introduces angular Doppler. The research shows that the rotation of the MS results in a non-stationary random process as the CCF. The only exception in which the result follows a stationary function is a narrow-band communication system, when either the MS angular velocity or the MS speed is zero.

The performance of an adaptive filter is restricted by the statistical behavior of the additive noise. The aim of this paper is to improve the convergence speed and steady state error of the set-membership normalized least mean square (SM-NLMS) algorithm in a colored noise environment. The noise is assumed to follow an auto-regressive (AR) model with bounded excitation uncorrelated samples. Without information about the noise parameters, the traditional SM-NLMS algorithm results in an unsatisfactory performance. A new simple SM algorithm is introduced to estimate the channel and the noise parameters simultaneously. The proposed algorithm efficiently exploits the redundant information of the noise to combat the noise. Theoretical results and simulations illustrate that the proposed algorithm has remarkable performance improvement over the NLMS and the traditional SM-NLMS algorithms. This proposed is successfully applied to a decision-directed algorithm for a QPSK communication scheme over an ISI channel with heavily colored noise environment.

A new efficient speech enhancement algorithm (SEA) is developed. Noisy speech is first decorrelated and then the clean speech components are estimated from the decorrelated noisy speech samples. The distributions of clean speech and noise are assumed to be Laplacian and Gaussian, respectively. The clean speech components are estimated either by maximum likelihood (ML) or minimum-mean-square-error (MMSE) estimators. These estimators require some statistical parameters that are adaptively extracted by the ML approach during the active speech or silence intervals, respectively. A voice activity detector (VAD) is employed to detect whether the speech is active or not. The simulation results show that this SEA performs as well as a recent high efficiency SEA that employs the Wiener filter. The complexity of this algorithm is very low compared with existing SEAs.

The paper first formulates the error propagation effect that is caused by certain ambiguities in joint data detection/channel tracking algorithms for space-time (ST) schemes. The occurrence of any combination of two types of ambiguities, phase ambiguity (PhA) and permutation ambiguity (PeA), initiated in deep fades, produces error propagation in joint channel estimation/data detection for the dual transmit diversity method. A new time-varying space-time coding (TVST) scheme is suggested as a bandwidth efficient method to combat the PeA impairment. This coding scheme, in conjunction with a differential detector, can resolve the ambiguity problem. Finally, some remedies used to prevent error propagation are listed. These remedies are called space-time ambiguity remedies (STARs).

A variable step-size (VS) algorithm is proposed based on the pre-filter bank (pfb) adaptive algorithm, first introduced by Courville and Duhamel (1998). The proposed algorithm adjusts the step-sizes of the subbands by using a simplified version of the Benveniste procedure (Ang et al. (2001)). As the filter banks are commonly narrowband filters, their nondecimated outputs are highly correlated. This correlation allows us to approximate the subband autocorrelation matrices by single rank matrices, thus permitting us to simplify and reduce the computational complexity of the VS procedures. The proposed inexpensive algorithm is very efficient in terms of tracking capabilities and initial learning for environments with colored additive noise and colored input signal.

A receiver for a dual transmit diversity system is designed that takes into account the channel estimation error, assuming the unknown channel to have a given complex bivariate Gaussian probability density function (PDF) (i.e., a Rician PDF). This criterion of the receiver, which is based on the maximum a posteriori (MAP) algorithm, is expressed in a quadratic form, and in extreme cases, represents either a linear detector or a non-coherent-non-linear detector. Simulations of the symbol error probability (SEP) of the receiver and analysis of an upper bound (UB) and a lower bound (LB) confirm that the proposed detector achieves robust performance against channel imperfections.

It is demonstrated that the distribution of speech samples is well described by Laplacian distribution (LD). The widely known speech distributions, i.e., LD, Gaussian distribution (GD), generalized GD, and gamma distribution, are tested as four hypotheses, and it is proved that speech samples during voice activity intervals are Laplacian random variables. A decorrelation transformation is then applied to speech samples to approximate their multivariate distribution. To do this, speech is decomposed using an adaptive Karhunen-Loeve transform or a discrete cosine transform. Then, the distributions of speech components in decorrelated domains are investigated. Experimental evaluations prove that the statistics of speech signals are like a multivariate LD. All marginal distributions of speech are accurately described by LD in decorrelated domains. While the energies of speech components are time-varying, their distribution shape remains Laplacian.

A new voice activity detector (VAD) is developed in this paper. The VAD is derived by applying a Bayesian hypothesis test on decorrelated speech samples. The signal is first decorrelated using an orthogonal transformation, e.g., discrete cosine transform (DCT) or the adaptive Karhunen-Loeve transform (KLT). The distributions of clean speech and noise signals are assumed to be Laplacian and Gaussian, respectively, as investigated recently. In addition, a hidden Markov model (HMM) is employed with two states representing silence and speech. The proposed soft VAD estimates the probability of voice being active (VBA), recursively. To this end, first the a priori probability of VBA is estimated/predicted based on feedback information from the previous time instance. Then the predicted probability is combined/updated with the new observed signal to calculate the probability of VBA at the current time instance. The required parameters of both speech and noise signals are estimated, adaptively, by the maximum likelihood (ML) approach. The simulation results show that the proposed soft VAD that uses a Laplacian distribution model for speech signals outperforms the previous VAD that uses a Gaussian model.

A set-membership (SM) normalized least-mean-square (NLMS) (SMNLMS) algorithm is developed using SM theory in the class of optimal bounding ellipsoid (OBE) algorithms. This signed version of NLMS algorithm requires a priori knowledge of a bound for the error magnitude, which is unknown in most applications. A very simple algorithm is proposed for the case in which the unknown magnitude of the measurement noise is slowly time-varying. The proposed algorithm is able to extract the noise magnitude information and exploit this magnitude to enhance or accelerate the learning process without risk of overbounding or performance loss due to underbounding. The performance of the proposed algorithm is compared with that of SMNLMS using some simulation examples.

A synchronized per survivor processing (PSP) maximum likelihood sequence detection (MLSD) receiver is presented for a fast frequency-selective Rayleigh fading channel and large carrier frequency offset. It is shown that, by incorporating the synchronization parameters (the timing error, carrier frequency and phase offsets) into the channel-impulse response (CIR) and modifying the resulting state-space model by a differential approach, the time-varying carrier frequency offset can be effectively tracked using a simple least mean square (LMS) estimator. New criteria are proposed based on the smoothness of the estimated channel derivatives. New criteria are proposed based on this fact that, for smoothly time-varying CIR and frequency offset, the magnitude of the CIR time derivative compared with the magnitude of CIR should be very small. It is illustrated that the new criteria and the conventional mean square error (MSE) are complementary.

We present a combined direct and indirect adaptive control scheme for adjusting an adaptive fuzzy controller, and adaptive fuzzy identification model parameters. First, using adaptive fuzzy building blocks, with a common set of parameters, we design and study an adaptive controller and an adaptive identification model that have been proposed for a general class of uncertain structure nonlinear dynamic systems. We then propose a hybrid adaptive (HA) law for adjusting the parameters. The HA law utilizes two types of errors in the adaptive system, the tracking error and the modeling error. Performance analysis using a Lyapunov synthesis approach proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence. Furthermore, this is achieved at negligible increased implementation cost or computational complexity. We prove a theorem that shows the properties of this hybrid adaptive fuzzy control system, i.e., bounds for the integral of the squared errors, and the conditions under which these errors converge asymptotically to zero are obtained. Finally, we apply the hybrid adaptive fuzzy controller to control a chaotic system, and the inverted pendulum system.

An adaptive Karhunen-Loeve transform (KLT) tracking-based algorithm is proposed for enhancement of speech degraded by colored additive interference. This algorithm decomposes noisy speech into its components along the axes of a KLT-based vector space of clean speech. It is observed that the noise energy is disparately distributed along each eigenvector. These energies are obtained from noise samples gathered from silence intervals between speech samples. To obtain these silence intervals, we proposed an efficient voice activity detector based on outputs of the principle component eigenfilter; the greatest eigenvalue of speech KLT. Enhancement is performed by modifying each KLT component due to its noise and clean speech energies. The objective is to minimize the produced distortion when residual noise power is limited to a specific level. At the end, the inverse KLT is performed and an estimation of the clean signal is synthesized. Our listening tests indicated that 71% of our subjects preferred the enhanced speech by the above method over former methods of enhancement of speech degraded by computer generated white Gaussian noise. Our method was preferred by 80% of our subjects when we processed real samples of noisy speech gathered from various environments.

A new algorithm is proposed for enhancement of speech degraded by colored additive interference. It is observed that the noise energy is disparately distributed along each eigenvector. These energies are obtained from noise samples gathered from silence intervals between speech samples. To obtain these silence intervals, we proposed an efficient voice activity detector based on outputs of principle component eigenfilter. Enhancement is performed by modifying each component due to its noise and clean speech energies. Tests indicated that 71% of our listeners preferred the enhanced speech by the above method over former methods for white Gaussian noise, while 80% of them preferred our method when we processed real noisy speech gathered from various environments.

This paper presents a maximum likelihood sequence detection (MLSD) receiver which performs joint data detection, channel equalization and synchronization over a fast frequency-selective Rayleigh fading channel. An optimal per-survivor processing (PSP) technique is employed which is a combination of statistical channel modeling and differential Kalman filter. It is shown that by incorporating the synchronization parameters into the channel impulse response and modification of the resulting state space model by a differential approach, frequency synchronization can be achieved by a simple adaptive algorithm in addition to data detection and channel estimation. It is also shown that by applying the so called approach, the differential Kalman filter could be simplified to a simple LMS estimator without any considerable lack in performance.

A decision-directed carrier-recovery technique for coherent demodulation of quadrature amplitude modulated (QAM) signals is proposed. This system adaptively tracks both the carrier frequency and phase of the received QAM signal's modulated carrier. The acquisition time of the proposed system is shown to be faster than that of conventional carrier-recovery schemes, especially when the initial carrier-frequency offset is relatively large, i.e. beyond a small fraction of the symbol rate. Also, the proposed method has a wider acquisition range compared to conventional techniques.

We consider the problem of locating and extracting sources received by an array of sensors in two significant cases. In one case, sensor positions are partially unknown. In the other one, the propagation to a subset of sensors is unknown. The core of the algorithm is partially blind identification of steering vectors. It relies on a tracking procedure with low computational complexity. We apply it to the calibration of maneuvering and towed arrays receiving multiple and correlated sources.

We present a novel adaptive algorithm in the frequency domain with a low order of arithmetic complexity for simultaneously performing beamforming, source direction finding and array shape calibration. The algorithm is proposed for multiple wideband sources, but could be applied to the narrowband case or to a single wideband source. The source signals are first estimated using a set of beamformers. These estimates are processed with the observation signals to track the steering vectors within the signal subspace. The adapted steering vectors are projected over the array manifold, to finally estimate the source directions and sensor positions. Simulations show the efficiency of the algorithm to achieve the proposed tasks.

Sensor positioning has an important influence on the performance of array processing. The main contributions of this paper are criteria to evaluate and to optimize the performance of an array of sensors through its geometric configuration-the number and the positions of sensors. The distance between the observed and the desired directivity patterns has been used as a criterion. We propose a generalization of this criterion. We obtain a second criterion by an orthogonality measurement between two subspaces, respectively, containing the noise and the desired signals. This measurement is obtained by an integration of the variance of the signal estimated by an unbiased linear estimator. The output power of a minimum variance distortionless response (MVDR) filter is considered as another criterion. Considering the channels between the desired sources and the array as a multidimensional communication channel, we obtain as a criterion the channel information capacity, which shows the maximum information that we may transfer by this channel. The simulation results confirm that the criteria are related and properly characterize the array configuration. For a linear equally spaced array and narrowband plane-wave signals, the classical results regarding the sensor spacing are confirmed.>

This paper presents an algorithm implementing robustness in beamforming, by directly reducing localization errors in the presence of pointing errors or a single moving target. Given an initial position, the desired source signal is first estimated using a classical beamforming unit. It is in a second step, processed by an LMS-like gradient stochastic estimation procedure of the steering vector, to adaptively track the correct source position. The newly identified source position is projected over the array manifold, then finally transmitted in a feedback loop to the beamforming unit, closing in this way the global algorithm iteration. The simulation results show that robustness is effectively realized without any compromising output SNR loss. Moreover, they prove an efficient tracking behavior in the presence of mobile sources. >

Sensor positioning has an important influence on the performance of array processing. The main contributions of the paper are the proposal of criteria to evaluate and to optimize the performance of an array of sensors through its geometric configuration, the number and the positions of the sensors. Each criterion evaluates the performance of the array for some applications, particularly for beamforming and source localization. The simulation results show that the criteria are related, and allow proper optimization of the array configuration.>

The performance of the transform domain normalized least mean square (LMS) algorithm is studied. The discussion is limited to the systems that employ an orthogonal transform to convert the incoming samples to a set of partially uncorrelated components, to be used as input to an adaptive linear combiner. To obtain an effective mathematical tool for analysis of the LMS-type algorithms a new performance index is proposed. The proposed index is used to derive many useful results. It is shown that a thorough understanding of the filtering concept results in many rules of thumb that can be readily used for choosing near-optimum transformations in the cases when partial information of input statistics is available. It is shown that the KLT (Karhunen-Loeve transform) implements a set of parallel filters which is optimal in the sense that it fulfils the conditions imposed by the filtering concept.>