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.

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.

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.

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.

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.

Receiver-channel based adaptive blind equalization approach for GPS dynamic multipath mitigation

Aiming at mitigating multipath effect in dynamic global positioning system （GPS） satellite navigation applications, an approach based on channel blind equalization and real-time recursive least square （RLS） algorithm is proposed, which is an application of the wireless communication channel equalization theory to GPS receiver tracking loops. The blind equalization mechanism builds upon the detection of the correlation distortion due to multipath channels; therefore an increase in the number of correlator channels is required compared with conventional GPS receivers. An adaptive estimator based on the real-time RLS algorithm is designed for dynamic estimation of multipath channel response. Then, the code and carrier phase receiver tracking errors are compensated by removing the estimated multipath components from the correlators＇ outputs. To demonstrate the capabilities of the proposed approach, this technique is integrated into a GPS software receiver connected to a navigation satellite signal simulator, thus simulations under controlled dynamic multipath scenarios can be carried out. Simulation results show that in a dynamic and fairly severe multipath environment, the proposed approach achieves simultaneously instantaneous accurate multipath channel estimation and significant multipath tracking errors reduction in both code delay and carrier phase.

Optimized blind equalization for probabilistically shaped high-order QAM signals

Probabilistically shaped(PS)high-order quadrature amplitude modulation(QAM)signals are attractive to coherent optical communication due to increased spectral efficiency.However,standard digital signal processing algorithms are not optimal to demodulate PS high-order QAM signals.Therefore,a compromise equalization is indispensable to compensate the residual distortion.Meanwhile,the performance of conventional blind equalization highly depends on the accurate amplitude radius and distribution of the signals.The PS high-order QAM signals make the issue worsen because of indistinct amplitude distributions.In this work,we proposed an optimized blind equalization by utilizing a peak-density K-means clustering algorithm to accurately track the amplitude radius and distribution.We experimentally demonstrated the proposed method in a PS 256-QAM coherent optical transmission system and achieved approximately 1 dB optical signal-to-noise ratio improvement at the bit error rate of 1×10^(−3).

Blind equalization and automatic modulation classification based on subspace for subcarrier MPSK optical communications

Equalization can compensate channel distortion caused by channel multipath effects, and effectively improve convergent of modulation constellation diagram in optical wireless system. In this paper, the subspace blind equalization algorithm is used to preprocess M-ary phase shift keying(MPSK) subcarrier modulation signal in receiver. Mountain clustering is adopted to get the clustering centers of MPSK modulation constellation diagram, and the modulation order is automatically identified through the k-nearest neighbor(KNN) classifier. The experiment has been done under four different weather conditions. Experimental results show that the convergent of constellation diagram is improved effectively after using the subspace blind equalization algorithm, which means that the accuracy of modulation recognition is increased. The correct recognition rate of 16 PSK can be up to 85% in any kind of weather condition which is mentioned in paper. Meanwhile, the correct recognition rate is the highest in cloudy and the lowest in heavy rain condition.

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.

A Hybrid Algorithm of BP Network and Its Application to Blind Equalization

We propose a novel hybrid algorithm of BP neural ytetwork and apply it to blind equalization.The algorithm combines the merits of Rosario algorithm and random optimization method. Its cost function has strict convex character (after a threshold) and the algorithm converges much faster than the BPmethod[2], as an exalople, we evaluate its performance by using it into blind equalization. With the help ofHigher Order Culnulants (HOC), the blind equalization scheme converges much faster than the CMAalgorithm and superior to the Back-propagation,nethod[2] due to its ability of finding the optimal solutionwith relatively fewer iteration stops.

Blind equalization of underwater acoustic channel using a combined constant modulus algorithm

A kind of Combined Constant Modulus Algorithm (CCMA) is presented to compensate the defects of the Constant Modulus Algorithm (CMA) and the Sign Error CMA (SECMA). And CCMA is applied to the equalization of the underwater acoustic channel (UWAC). Based on the decision of the equalizer’s output, its iteration process switches between

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.

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.

Adaptive blind equalizer based on least square support vector machine

An adaptive blind support vector machine equalizer（ABSVME） is presented in this paper.The method is based upon least square support vector machine（LSSVM）,and stems from signal feature reconstruction idea.By oversampling the output of a LSSVM equalizer and exploiting a reasonable decorrelation cost function design,the method achieves fine online channel tracing with Kumar express algorithm and static iterative learning algorithm incorporated.The method is verified through simulation and compared with other nonlinear equalizers.The results show that it provides excellent performance in nonlinear equalization and time-varying channel tracing.Although a constant module equalization algorithm requires that the signal has characteristic of constant module,this method has no such requirement.

VLSI Implementation of a Single-Chip DVB-C Demodulator

A single-chip DVB-C quadrature amplitude modulation（QAM） demodulator is proposed,which integrates a 3.3V 10bit 40MSPS analog-to-digital converter and a forward error correction decoder. The demodulator chip can support 4-256 QAM with variable bit rate up to 80Mbps. It features a wide carrier offset acquisition range,optimal demodulation algorithm,and small circuit area. The chip is implemented in SMIC 0.25μm 1P5M mixed-signal CMOS technology with a die size of 3.5mm×3. 5mm. The maximum power consumption is 447mW.

A Specially Designed Modulation-Induced Cyclostationarity Scheme for Blind SISO Channel Identification and Equalization

A special Modulation-Induced Cyclostationarity(MIC)scheme is designed for the identification and equaliza-tion of FIR Single-Input-Single-Output(SISO)channel,with the property that the transmit power is constant and the re-ceiver needs only one antenna.The cyclic Wiener equalizer is presented based on the estimated channel.

WIDELY LINEAR RLS CONSTANT MODULUS ALGORITHM FOR COMPLEX-VALUED NONCIRCULAR SIGNALS

Based on the constant modulus criterion, a new Widely Linear(WL) blind equalizer and a novel widely linear recursive least square constant modulus algorithm are proposed to improve the blind equalization performance for complex-valued noncircular signals. The new algorithm takes advantage of the WL filtering theory by taking full use of second-order statistical information of the complex-valued noncircular signals. Therefore, the weight vector contains the complete second-order information of the real and imaginary parts to decrease the residual inter-symbol interference effectively. Theoretical analysis and simulation results show that the proposed scheme can significantly improve the equalization performance for complex-valued noncircular signals compared with traditional blind equalization algorithms.

Comparative Study on Channel Compensation for Robust Speech Recognition

Some channel compensation techniques integrated into front-end of speech recognizer for improving channel robustness are described. These techniques include cepstral mean normalization, rasta processing and blind equalization. Two standard channel frequency characteristics, G.712 and MIRS, are introduced as channel distortion references and a mandarin digit string recognition task is performed for evaluating and comparing the performance of these different methods. The recognition results show that in G.712 case blind equalization can achieve the best recognition performance while cepstral mean normalization outperforms the other methods in MIRS case which is capable of reaching a word error rate of 3.96%.

Blind Equalization Using a Novel Recurrent Neural Network Training Algorithm

A novel Recurrent Neural Network(RNN) based blind equalization algorithm is proposed in the paper.For the first time, the conjugate gradient algorithm and a three point searching method are used in RNN training.Simulation results show that our algorithm is suprior to the one proposed by Kechriotis and the Constant Modulus Algorithm.