A New Approach for on-Line Blind Equalization and Channel Identification

A new approach for blind equalization and channel identification is proposed in this paper. The equalization scheme is based on over sampling technique and an independent component analysis network. The equalized sequence and its higher order statistics are used to identify the channel parameters. Compared to traditional equalization methods, the proposed approach is with a simple architecture, and does not need learning sequences. Computer simulations show the validity of the proposed method.

Blind Equalization Based on RLS Algorithm Using Adaptive Forgetting Factor for Underwater Acoustic Channel

Blind equalization based on adaptive forgetting factor, recursive least squares （RLS） with constant modulus algorithm （CMA）, is investigated. The cost function of CMA is simplified to meet the second norm form to ensure the stability of RLS-CMA, and thus an improved RLS-CMA （RLS-SCMA） is established. To further improve its performance, a new adaptive forgetting factor RLS-SCMA （ARLS-SCMA） is proposed. In ARLS-SCMA, the forgetting factor varies with the output error of the blind equalizer during the iterative process, which leads to a faster convergence rate and a smaller steady-state error. The simulation results prove the effectiveness under the condition of the underwater acoustic channel.

Modulation Recognition in Maritime Multipath Channels:A Blind Equalization-Aided Deep Learning Approach

Modulation recognition has been long investigated in the literature,however,the performance could be severely degraded in multipath fading channels especially for high-order Quadrature Amplitude Modulation(QAM)signals.This could be a critical problem in the broadband maritime wireless communications,where various propagation paths with large differences in the time of arrival are very likely to exist.Specifically,multiple paths may stem from the direct path,the reflection paths from the rough sea surface,and the refraction paths from the atmospheric duct,respectively.To address this issue,we propose a novel blind equalization-aided deep learning(DL)approach to recognize QAM signals in the presence of multipath propagation.The proposed approach consists of two modules:A blind equalization module and a subsequent DL network which employs the structure of ResNet.With predefined searching step-sizes for the blind equalization algorithm,which are designed according to the set of modulation formats of interest,the DL network is trained and tested over various multipath channel parameter settings.It is shown that as compared to the conventional DL approaches without equalization,the proposed method can achieve an improvement in the recognition accuracy up to 30%in severe multipath scenarios,especially in the high SNR regime.Moreover,it efficiently reduces the number of training data that is required.

Blind Channel Equalization Algorithm Based on Dual Unscented Kalman Filter for Chaotic Multi-Input Multi-Output Communication Systems

To reduce channel noise,fading,and inter-user interference effectively in the chaotic communication systems with multi-user,a blind channel equalization algorithm based on dual unscented Kalman filter algorithm is proposed.Assuming that the coefficients of a multi-input multi-output (MIMO) channel can be described by an autoregressive model,two separate state-space representations are used for the signals and coefficients.Then two unscented Kalman filters are used to estimate chaotic signals and channel coefficients simultaneously.The simulation results indicate that the algorithm can effectively track the coefficients of the multi-path fading channel in chaotic MIMO communication systems at a fast convergence speed.

Blind Equalization Algorithm Based on MCMA and DSE-CMA

A new blind equalization algorithm based on the modified constant modulus algorithm （MCMA） and dithered signederror constant modulus algorithm （DSE-CMA） is proposed. This dithered signed-error MCMA （DSE-MCMA） can not only reduce the computational complexity, but also recover the phase rotation in the complex channel. Simulation results have verified the analysis and indicated the good property of DSE-MCMA.

A Modified T/2 Fractionally Spaced Coordinate Transformation Blind Equalization Algorithm

When T/2 Fractionally Spaced blind Equalization Algorithm based Constant Modulus Algorithm (T/2-FSE- CMA) is employed for equalizing higher order Quadrature Amplitude Modulation signals (QAM), it has disadvantages of low convergence speed and large Mean Square Error (MSE). For overcoming these disadvantages, a Modified T/2 Fractionally Spaced blind Equalization algorithm based on Coordinate Transformation and CMA (T/2-FSE-MCTCMA) was proposed by analyzing the character of 16QAM signal constellations. In the proposed algorithm, real and imaginary parts of input signal of T/2 fractionally spaced blind equalizer are equalized, respectively, and output signals of equalizer are transformed to the same unit circle by coordinate transformation method, a new error function is defined after making coordinate transformation and used to adjust weight vector of T/2 fractionally spaced blind equalizer. The proposed algorithm can overcome large misjudgments of T/2 fractionally spaced blind equalization algorithm for equalizing multi-modulus higher order QAM. Simulation results with underwater acoustic channel models demonstrate that the proposed T/2-FSE-MCTCMA algorithm outperforms T/2 Fractionally Spaced blind Equalization algorithm bas-ed on Coordinate Transformation and CMA (T/2-FSE-CTCMA) and the T/2-FSE-CMA in convergence rate and MSE.

BLIND EQUALIZATION OF MIMO SYSTEMS BASED ON ORTHOGONAL CONSTANT MODULUS ALGORITHM

This paper investigates adaptive blind source separation and equalization for Multiple Input Multiple Output (MIMO) systems. To effectively recover input signals, remove Inter-Symbol Interference (ISI) and suppress Inter-User Interference (IUI), the array input is first transformed into the signal subspace, then with the derived orthogonality between weight vectors of different input signals, a new orthogonal Constant Modulus Algorithm (CMA) is proposed. Computer simulation results illustrate the promising performance of the proposed method. Without channel identification, the proposed method can recover all the system inputs simultaneously and can be adaptive to channel changes without prior knowledge about signals.

A New Equalization Performance Analyzing Method for Blind Adaptive Equalizers Inspired by Maximum Time Interval Error

Up to now, the Mean Square Error (MSE) criteria, the residual Inter-Symbol Interference (ISI) and the Bit-Error-Rate (BER) were used to analyze the equalization performance of a blind adaptive equalizer in its convergence state. In this paper, we propose an additional tool (additional to the ISI, MSE and BER) for analyzing the equalization performance in the convergence region based on the Maximum Time Interval Error (MTIE) criterion that is used for the specification of clock stability requirements in telecommunications standards. This new tool preserves the short term statistical information unlike the already known tools (BER, ISI, MSE) that lack this information. Simulation results will show that the equalization performance of a blind adaptive equalizer obtained in the convergence region for two different channels is seen to be approximately the same from the residual ISI and MSE point of view while this is not the case with our new proposed tool. Thus, our new proposed tool might be considered as a more sensitive tool compared to the ISI and MSE method.

An Orthogonal Wavelet Transform Fractionally Spaced Blind Equalization Algorithm Based on the Optimization of Genetic Algorithm

An orthogonal wavelet transform fractionally spaced blind equalization algorithm based on the optimization of genetic algorithm(WTFSE-GA) is proposed in viewof the lowconvergence rate,large steady-state mean square error and local convergence of traditional constant modulus blind equalization algorithm(CMA).The proposed algorithm can reduce the signal autocorrelation through the orthogonal wavelet transform of input signal of fractionally spaced blind equalizer,and decrease the possibility of CMA local convergence by using the global random search characteristics of genetic algorithm to optimize the equalizer weight vector.The proposed algorithm has the faster convergence rate and smaller mean square error compared with FSE and WT-FSE.The efficiency of the proposed algorithm is proved by computer simulation of underwater acoustic channels.

Blind Adaptive MMSE Equalization of Underwater Acoustic Channels Based on the Linear Prediction Method

The problem of blind adaptive equalization of underwater single-input multiple-output (SIMO) acoustic channels was analyzed by using the linear prediction method.Minimum mean square error (MMSE) blind equalizers with arbitrary delay were described on a basis of channel identification.Two methods for calculating linear MMSE equalizers were proposed.One was based on full channel identification and realized using RLS adaptive algorithms,and the other was based on the zero-delay MMSE equalizer and realized using LMS and RLS adaptive algorithms,respectively.Performance of the three proposed algorithms and comparison with two existing zero-forcing (ZF) equalization algorithms were investigated by simulations utilizing two underwater acoustic channels.The results show that the proposed algorithms are robust enough to channel order mismatch.They have almost the same performance as the corresponding ZF algorithms under a high signal-to-noise (SNR) ratio and better performance under a low SNR.

Application of Recurrent Wavelet Neural Networks to the Digital Communications Channel Blind Equalization

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SOME BLIND FRESH EQUALIZATION ALGORITHMS WITH ANTI-INTERFERENCE CAPABILITIES

Some novel blind FREquency-SHift (FRESH) equalizer algorithms are proposed for the equalization of Finite Impulse Response (FIR) single channel with anti-interference capabilities. These algorithms based on FRESH filter can work well without any training sequence. Simulation results show that the equalizer algorithms can effectively reject many types of interferences and the performances of these new equalizer algorithms are superior to the conventional equalizer algorithms.

Joint receiving mechanism based on blind equalization with variable step size for M-QAM modulation

The problem of inter symbol interference（ ISI） in wireless communication systems caused by multipath propagation when using high order modulation like M-Q AMis solved. Since the wireless receiver doesn＇t require a training sequence,a blind equalization channel is implemented in the receiver to increase the throughput of the system. To improve the performances of both the blind equalizer and the system,a joint receiving mechanismincluding variable step size（ VSS） modified constant modulus algorithms（ MC-MA） and modified decision directed modulus algorithms（ MD DMA） is proposed to ameliorate the convergence speed and mean square error（ MSE） performance and combat the phase error when using high order QAM modulation. The VSS scheme is based on the selection of step size according to the distance between the output of the equalizer and the desired output in the constellation plane. Analysis and simulations showthat the performance of the proposed VSS-MCMA-MD DMA mechanismis better than that of algorithms with a fixed step size. In addition,the MCMA-MDDMA with VSS can performthe phase recovery by itself.

An Adaptive Momentum CMA Blind Equalization Based on Error Energy

To solve the problem of large steady state residual error of momentum constant modulus algorithm (CMA) blind equalization, a momentum CMA blind equalization controlled by energy steady state was proposed. The energy of the equalizer weights is estimated during the updating process. According to the adaptive filtering theory, the energy of the equalizer weights reaches to the steady state after the algorithm is converged, and then the momentum can be set to 0 when the energy change rate is less than the threshold, which can avoid the additional gradient noise caused by momentum and further improve the convergence precision of the algorithm. The proposed algorithm takes advantage of momentum to quicken the convergence rate and to avoid the local minimum in the cost function to some extent;meanwhile, it has the same convergence precision with CMA. Computer simulation results show that, compared with CMA, momentum CMA (MCMA) and adaptive momentum CMA (AMCMA) blind equalization, the proposed algorithm has the fastest convergence rate and the same steady state residual error with CMA.

Blind Equalization Using a Hybrid Algorithm of Multilayer Neural Network

A novel blind equalization scheme based on multilayer neural network and Higher OrderCumulants(HOC)is proposed in the paper.The training of the neural network uses a newhybrid algorithm which has strict convex character(after a threshold)and converges muchfaster than the CMA algorithm.The inverse channel is built on the basis of the estimatedchannel and the training of neural network.The scheme can be used in nonlinear and timevarying channel and to deal with PAM or QAM signals.Simulation results Show that it per-forms well for blind equalization.

Two Blind Adaptive Equalizers Connected in Series for Equalization Performance Improvement

A variable step-size parameter is usually used to accelerate the convergence speed of a blind adaptive equalizer with N1 + N2 -1 coefficients where N1 and N2 are odd values. In this paper we show that improved equalization performance is achieved when using two blind adaptive equalizers connected in series where the first and second blind adaptive equalizer have N1 and N2 coefficients respectively compared with the case where a single blind adaptive equalizer is applied with N1 + N2 -1 coefficients. It should be pointed out that the same algorithm (cost function) is used for updating the filter taps for the different equalizers and that a fixed step-size parameter is used. Simulation results show that for the low signal to noise ratio (SNR) environment and for the case where the convergence speed is slow due to the channel characteristics, the new method has a faster convergence speed with a factor of approximately two while leaving the system with approximately the same or lower residual intersymbol interference (ISI).

The Fast Blind Equalization Algorithm with Global Convergence

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Blind Equalization Based on Evolution Strategies

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A Blind Batch Encryption and Public Ledger-Based Protocol for Sharing Sensitive Data

For the goals of security and privacy preservation,we propose a blind batch encryption-and public ledger-based data sharing protocol that allows the integrity of sensitive data to be audited by a public ledger and allows privacy information to be preserved.Data owners can tightly manage their data with efficient revocation and only grant one-time adaptive access for the fulfillment of the requester.We prove that our protocol is semanticallly secure,blind,and secure against oblivious requesters and malicious file keepers.We also provide security analysis in the context of four typical attacks.

Countermeasure against blinding attack for single-photon detectors in quantum key distribution

Quantum key distribution(QKD),rooted in quantum mechanics,offers information-theoretic security.However,practi-cal systems open security threats due to imperfections,notably bright-light blinding attacks targeting single-photon detectors.Here,we propose a concise,robust defense strategy for protecting single-photon detectors in QKD systems against blinding attacks.Our strategy uses a dual approach:detecting the bias current of the avalanche photodiode(APD)to defend against con-tinuous-wave blinding attacks,and monitoring the avalanche amplitude to protect against pulsed blinding attacks.By integrat-ing these two branches,the proposed solution effectively identifies and mitigates a wide range of bright light injection attempts,significantly enhancing the resilience of QKD systems against various bright-light blinding attacks.This method forti-fies the safeguards of quantum communications and offers a crucial contribution to the field of quantum information security.