Blind Equalization

Circular harmonic expansion-based carrier frequency offset estimation is investigated for optical MQAM communication systems. The proposed method, combined with a gradient-descent algorithm, shows better performance compared to already proposed VVMFOE and 4 th power methods.

We present a novel simple blind adaptive compensation method for in-phase/quadrature (IQ) imbalance in m-ary quadrature amplitude modulation (mQAM) coherent optical fiber communication systems. IQ-imbalance compensation is integrated into butterfly-structured finite impulse response (FIR) filters, resulting in a significant computational effort reduction in comparison to conventional methods. A reduction in hardware complexity by a factor of about 3 is achieved by the proposed joint method. The proposed structure is experimentally validated with a 40Gbit/s 16-QAM signal. A 7-dB power penalty reduction is experimentally achieved at a bit error rate (BER) of 10 -3 in the presence of a 10° phase imbalance, confirming the effectiveness of the proposed algorithm. The equalization capability remains even in the presence of group velocity dispersion along the link, which is numerically confirmed with optical fiber transmission up to 1200 km and 20 phase imbalance.

This paper addresses the problem of blind equalization based on Higher-Order statistics. In fact, we propose a new method for blind identification which uses only fourth order cumulants. The Channel impulse response coefficients are obtained by using a least squares method. Next, we exploit this method in the context of blind equalization. Numerical examples are also presented in order to illustrate the performances of the proposed method.

We obtain the convergence of the Godard family (including SATO and CM algorithms) in a unified way. Our assumptions are quite realistic: the channel input can be asymptotically stationary and ergodic, the channel impulse response is finite and can be stationary, ergodic (this models fading channels) and the equalizer length is finite. The noise is i.i.d. The channel input can be discrete or continuous. Our approach allows us to approximate the whole trajectory of the equalizer coefficients. This provides estimates of the rate of convergence and the system performance (symbol error rate) can be evaluated under transience and steady state.

The simultaneous transmission of coherent signals with intensity-modulated direct-detection (IM-DD) signals within a single optical fiber offers economic benefits to network providers. However, this coexistence can pose challenges, of particular concern are parasitic nonlinear effects such as cross-phase modulation (XPM). In a poorly implemented system, these effects have the potential to degrading the bit error rate (BER) of the coherent signal. Although DWDM system vendors are pushing for the exclusive deployment of coherent modulation formats, IM-DD signals-often referred to as legacy formats-remain relevant due to emerging applications such as highprecision time transfer and distributed fiber sensing. QKD and stable frequency transfer are examples of other new applications, but they are not as challenging because of the different modulation and spectral characteristics. Our findings quantify the extent of this degradation of coherent signals and show how frequency guardbands can be used to mitigate or even eliminate the problem.

In some communication systems, it is desirable for the receiver to synchronize to the received signal and to adjust the equalizer without having knowledge of a training sequence. Blind equalization uses the initial adjustment of the coefficients without making use of a training sequence. Different adaptive blind equalization algorithms have been developed over the past four decades. In this paper, we investigate the effect of blind equalization on space communication channels. The space channel under investigation is considered to be a multipath frequency selective channel having four paths. The channel is subjected to the phenomenon of InterSymbol Interference (ISI) which severely degrades the performance of the space communication system. Two blind algorithms are used in equalizer adjustment. The impulse responses of the space channel, the blind equalizer and the combination of channel and equalizer for QPSK and 16-QAM transmission are shown. The scatter diagrams for the transmitt...

Tensor decompositions provide a natural modeling framework for modern wireless communication and array processing systems. This survey presents a physicsdriven perspective on structured CP/PARAFAC models, identifiability conditions, and constrained factorizations. We emphasize how propagation mechanisms lead to multilinear separability, how diversity across modes ensures identifiability, and how reconfigurable architectures introduce controllable tensor dimensions. The discussion bridges wireless channel estimation and radar parameter recovery under a unified multilinear viewpoint.

This report provides a concise review of tensor decomposition models in wireless communications and array processing. Emphasis is placed on physics-driven tensorization, structured CP/PARAFAC models, identifiability conditions, RIS-induced higher-order tensor models, and practical algorithmic considerations. We highlight the interplay between propagation physics, multilinear algebra, and estimation theory.

Adaptive filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancellation [1], array beamforming [2], channel equalization [3], and to more recent sensor network applications in surveillance, target localization and tracking. A trending approach in this direction is to recur to in-network distributed processing in which individual nodes implement adaptation rules and diffuse their estimation to the network [4], . Ranging from the simple Least-Mean-Squares (LMS) to sophisticated state-space algorithms, significant research has been carried out over the last 50 years to develop effective adaptive algorithms, in an attempt to improve their general properties in terms of convergence, tracking ability, steady-state misadjustment, robustness, or computational cost . Many design procedures and theoretical models have been developed, and many novel adaptive structures are continually proposed with the objective of improving filter behavior with respect to well-known performance tradeoffs (such as convergence rate versus steady-state performance), or incorporating available a priori knowledge into the learning mechanisms of the filters (e.g., to enforce sparsity).

This letter introduces a new frequency domain approach for either MIMO System Identification or Source Separation of convolutive mixtures in cyclostationary context. We apply the joint diagonalization algorithm to a set of cyclic spectral density matrices of the measurements to identify the mixing system at each frequency up to permutation and phase ambiguity matrices. An efficient algorithm to overcome the frequency dependent permutations and to recover the phase, even for non-minimum-phase channels, based on cyclostationarity is also presented. The new approach exploits the fact that each input has a different and specific cyclic frequency. A comparison with an existing MIMO method is proposed.

Blind source separation presents an interest for several applications fields. In this study, focus is on the blind separation of biomechanical signals namely ground reaction force signals. We showed recently in , that ground reaction force signals are cyclostationary. Thus, the idea of this paper is to make use of the cyclostationary character of ground reaction force signals to separate it and then compare the separation results with those based on stationary statistics. To this end, we introduce frequency domain approaches for either blind source separation or MIMO system identification excited by cyclostationary inputs. These approaches exploit the cyclic spectra matrices of the whitened measurements to identify the mixing system at each frequency bin, up to constant diagonal, frequency dependent permutation and phase ambiguity matrices. Two efficient algorithms to fix the permutation problem and to remove the phase ambiguity based on cyclostationarity are also presented. The approaches exploit the fact that the input signals are cyclostationary with the same cyclic frequency. Simulation examples are presented to illustrate the performances of these approaches. Furthermore, the effectiveness of the proposed methods are tested over real signals and compared with existing methods, as well.

In this paper we present two algorithms for blind channel equalization. These algorithms are compared with the adaptive filter algorithms such as Constant Modulus Algorithm (CMA), Fractional Space CMA (FSCMA) and Sign Kurtosis Maximization Adaptive Algorithm (SKMAA). The simulation results in noisy environment and for different number of symbols show that the presented algorithms gives good results compared to CMA, FSCMA and SKMAA algorithms. The channel equalization is performed using the ZF and MMSE algorithms.

This paper propose an algorithm based on ZF and MMSE methods for blind channel equalization, which is compared with adaptive filter algorithms which are Constant Modulus Algorithm (CMA), Fractional Space CMA (FSCMA) and Sign Kurtosis Maximization Adaptive Algorithm (SKMAA). The simulations show that the proposed algorithm gives satisfied result versus CMA, FSCMA and SKMAA algorithms. The study is done under certain conditions, it is implemented in noisy environment, for different number of symbols and different SNR values with QPSK modulation. Equalization of channel is more performing if we use the proposed algorithms.

This paper resolves the problem of channel estimation and identification of a nonminimum phase system using three algorithms. These algorithms play an important role for estimating blindly the parameters of radio mobile channel. Thus studying the blind's problem channel equalization based on the following, Proposed Algorithm, CMA and SKMAA equalizers. The simulation results in the noisy environment and for different SNR values demonstrate that Proposed Algorithm is more performing than the other algorithms. In addition, the Sign Kurtosis Maximization Adaptive Algorithm (SKMAA) is more powerful compared to Constant Modulus Algorithm (CMA), that is to say it gives the right blind channel equalization.

Les travaux de cette these portent sur l'egalisation des canaux de transmission pour des modulations mono-porteuses et multi-porteuses. Dans le cadre de l'egalisation, nous nous interessons, plus precisement, a l'egalisation aveugle. Tout d'abord, nous decrivons les differents elements constituants une chaine de communication, et les differents types de modulations mono-porteuses et muti-porteuses (OFDM). Ensuite, nous faisons un etat de l'art sur les methodes de l'egalisation aveugle pour une modulation mono-porteuse. Nous proposons par la suite un algorithme d'egalisation aveugle en presence de perturbations bornees. Ensuite, nous fournissons une analyse de stabilite et de convergence de l'algorithme propose. Dans le cadre de la modulation multi-porteuse, nous presentons, dans un premier temps, un etat de l'art sur les techniques d'egalisation aveugle pour le systeme OFDM. Ensuite, nous adaptons l'algorithme propose pour le systeme OFDM ...

In light of the recently proposed generalized correlation function named correntropy, which exploits higher-order statistics and the time structure of signals, we have, in this work, two main objectives: 1) to give a new interpretation -founded on the relationships between the constant modulus (CM) and Shalvi-Weinstein criteria and between the latter and methods for ICA based on nongaussianity -to the performance of the constant modulus approach under dependent sources and 2) to analyze the correntropy in the context of blind deconvolution of i.i.d. and dependent sources, as well as to establish elements of a comparison between it and the CMA. The analyses and simulation results unveil some theoretical aspects hitherto unexplored.

In this work, we study the criteria used to solve the blind equalization problem. Two approaches are considered in detail: the constant modulus and the Shalvi-Weinstein criteria. In the course of our exposition, a more recent and less studied technique, the generalized constant modulus criterion, is also discussed. Some of the most important results found in the literature are presented together with some recent contributions related to the comparison between blind criteria and between unsupervised techniques and the Wiener criterion.

A crucial performance measure in the context of the problem of deconvolution is the level of residual intersymbol interference (ISI), a metric that is classically understood and formulated in terms of an L2-norm perspective. In order to enhance the scope of ISI quantification, we propose a novel entropy-based performance measure, which is called Entropybased Intersymbol Interference (HISI). Interestingly, this metric is related to an information-theoretic relationship between source distribution and Lp-norms when the error entropy is used as a basis for optimal filtering. The new metric is analytically investigated and illustrated with some simulations.

From the classical blind CM criterion, we derive a MSE-based polynomial formulation that lead to two contributions: a lower bound of the CM criterion, which works as an equalizability index, and an initialization heuristic for the CMA. The results indicate the validity of the index as an analytical tool and as a practical performance assessment metric in the context of inverse problems. For the heuristic, the results reveal that it is capable of outperforming the classical center-spike method and a random initialization approach.

This paper shows that it is possible to achieve convergence to the optimum solution in a blind equalization framework with the use of least mean square algorithm in decision-directed mode (DD-LMS). In linear equalizers structures, the attainment of the optimum solution strongly depends on the filter weights initialization. We also show that decision-feedback equalizers in DD mode (DD-DFE) converges to undesired local minima, when all its weights are initialized with zeros, for a certain class of channels. However, it is ...

This paper considers the blind equalization problem for nonlinear channels of Volterra type excited by real i.i.d. symbols. Previous work has shown that under the right conditions the equalizers can be found from the second order statistics of the channel output as long as the number of subchannels exceeds the number of kernels. In order to alleviate this requirement, we consider the use of a simple precoding device previous to transmission which provides a tradeoff between effective data rate and number of subchannels required. Necessary and sufficient conditions for blind equalizability under this scheme are given, and an algorithm for the computation of the equalizers is presented.

In real-world scenarios, deviations in the frequency response of instrumentation transformers can lead to distorted harmonic measurements, highlighting the critical role harmonic measurement plays in assessing power quality. The blind channel equalization technique offers a potential solution to improve the frequency response of a large number of instrumentation transformers already installed in substations. These transformers were designed to accurately measure only the fundamental phasor component. Therefore, in order to use them for harmonic phasor measurement, methodologies for reducing frequency distortion must be applied. In this work, we propose a novel approach to improve the frequency response of the instrument transformer using adaptive blind equalization. The blind technique can compensate for distortions caused by voltage and current transducers without requiring prior knowledge of input signals or circuit characteristics. The proposed methodology uses a Linear Predictio...

We consider semi-blind data detectors for OFDM systems over fast fading channels. Three maximum-likelihood (ML) data detectors (MLD) are derived assuming exact channel correlation. The first two exploit the correlation among pre-DFT and post-DFT received signals and the third one average the ML metric over channel impulse response (CIR). We show that the first two MLDs for input data symbols are given by an integer least-squares (LS) minimization problem, which can be efficiently solved by V-BLAST detection (suboptimal) or by a sphere decoder (SD) (optimal). We contribute a low-complexity near-ML SD and an iterative detector for the third MLD. Despite its low complexity, MLD1 performs robustly for normalized Doppler rates less than 3%, which meets the requirement of many practical systems. With iterative detection, both MLD1 and MLD3 can exploit time-diversity.

In this paper, in order to compress and enhance 2D images transmitted over wireless channels, a new scheme called Kalman-Turbo (KT) is introduced. In this scheme, the original image is partitioned into 2 N quantization levels and each of the N-bit planes is coded by Turbo encoder. After noise corruption of the channel, each of the noisy bit planes is processed iteratively by a joint equalization block, which is composed of low-complexity 2D binary Kalman filter and the Turbo decoder. In our Kalman filter, state equations are modified in order to take the advantage of two-dimensionality, and, previous soft decisions are taken into account to avoid divergence of Kalman filter. Simulation results show that the performance of the KT system is superior at low SNRs when compared with that of plain Turbo decoding algorithms. We can also achieve compression by any selection of N. Hence, we conclude that KT system will be a compromising approach in 2D image transmission.

In this communication we present the first results of a project whose goal is to remove artifacts from electroencephalographic epileptic signals. More precisely the present objective is to remove ocular (blinking) artifacts in simulated and real EEG signal using Independent Component Analysis and wavelet denoising algorithms.

Recently, the marginalized particle filter (MPF) has been applied to blind symbol detection problems over selective fading channels. The MPF can ease the computational burden of the standard particle filter (PF) while offering better estimates compared with the standard PF. In this paper, we investigate the application of the blind MPF detector to more realistic situations where the systems suffer from analog imperfections which are non-linear signal distortion due to the inaccurate analog circuits in wireless devices. By reformulating the system model using the widely linear representation and employing the auxiliary variable resampling (AVR) technique for estimation of the imperfections, the blind MPF detector is successfully modified to cope with the analog imperfections. The effectiveness of the proposed MPF detector is demonstrated via computer simulations.