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.

SUBSPACE METHOD FOR BLIND IDENTIFICATION OF CDMA TIME-VARYING CHANNELS

A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant (TI) coordinates is proposed. Unlike existing basis expansion methods, this new algorithm does not require .estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols. The algorithm offers definite explanation of the expansion coordinates. Simulation demonstrates the effectiveness of the algorithm.

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.

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

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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 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.

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.

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.

Bi-iterative least squares algorithms for blind channel identification and equalization with second-order statistics

We present an adaptive algorithm for blind identification and equalization of single-input multiple-output （SIMO） FIR channels with second-order statistics. We first reformulate the blind channel identification problem into a low-rank matrix approximation solution based on the QR decomposition of the received data matrix. Then, a fast recursive algorithm is developed based on the bi-iterative least squares （Bi-LS） subspace tracking method. The new algorithm requires only a computational complexity of O（md2） at each iteration, or even as low as O（md） if only equalization is necessary, where m is the dimension of the received data vector （or the row rank of channel matrix） and d is the dimension of the signal subspace （or the column rank of channel matrix）. To overcome the shortcoming of the back substitution, an inverse QR iteration algorithm for subspace tracking and channel equalization is also developed. The inverse QR iteration algorithm is well suited for the parallel implementation in the systolic array. Simulation results are presented to illustrate the effectiveness of the proposed algorithms for the channel identification and equalization.

Blind Signal Processing: I-Fundamental Concepts

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Blind Signal Processing in Telecommunication Systems Based on Polynomial Statistics

The tendencies of the contemporary communication systems development are characterized by the increasingly stringent requirements for maximum channel utilization. Considering discrete communication systems in channels with intersymbol interference identification with the use of training signal is the key technology to create various types of equalizers. However, the time (from 20% to 50%) spent on training signal is increasingly attractive resource for upgrading standards TDMA, especially in mobile systems. An alternative method to training signal is blind signal processing.

脉冲噪声下AUV水声通信盲均衡算法模拟仿真

针对存在琢稳定分布噪声的水声信道中传统常数模盲均衡算法(constant modulus algorithm,CMA)无法稳定收敛的问题,提出一种基于模拟退火粒子群算法优化的变步长分数低阶盲均衡算法(SAPSO-VSS-FLOSCMA),该算法将FLOSCMA的代价函数作为SAPSO算法的适应度函数,利用SAPSO算法搜索全局最优解代替盲均衡器的初始权向量,降低了FLOSCMA算法的稳态误差,并采用双曲正切函数调节FLOSCMA算法的步长因子加快算法的收敛速度。水声信道仿真结果表明,与CMA和FLOSCMA算法相比,SAPSO-VSS-FLOSCMA算法在收敛速度和稳态误差方面均得到提高,且误码率更低。

Blind Multiuser Channel Identification Based on Rate Doubling and Modulation-Induced Cyclostationarity

In this paper, a novel approach is put forward to the multiuser channelidentification. The approach makes use of the modulation-induced Cyclostationarity to separate thesecond order cyclic statistics for every user, with the special features of one subspace for oneuser, so as to be able to identify individual channels of different users. In order to form aSingle-Input-Two-Output (SITO) system, the transmission rate is doubled by repeating at thetransmitters. The approach is rather simple, suitable for the multiuser uplink . And the channelidentifiability conditions with its proof are included in the paper, And finally the identificationalgorithm is proposed with simulation results.

Distributed and recursive blind channel identification to sensor networks

In this paper, the distributed and recursive blind channel identification algorithms are proposed for single-input multi-output （SIMO） systems of sensor networks （both time-invariant and time-varying networks）. At any time, each agent updates its estimate using the local observation and the information derived from its neighboring agents. The algorithms are based on the truncated stochastic approximation and their convergence is proved. A simulation example is presented and the computation results are shown to be consistent with theoretical analysis.

基于深层神经网络的信道编码类型盲识别

为了解决当前识别算法只能识别一种或者两种码字类型以及人工提取特征复杂的问题,提出了两种基于深层神经网络模型的信道编码类型识别器,即卷积神经网络(convolutional neural network,CNN)识别器和递归CNN(recursive CNN,RCNN)识别器,用于识别接收数据中不同类型的信道码字。将待识别的软解调序列作为自然语言处理中文本分类问题的句子向量进行处理,输入到预先训练好的深层神经网络识别器中进行识别,并分析了字长度对识别准确率的影响,得出了最合适的字长度。实验结果表明,两种识别器都能够有效识别接收数据中多种类型的信道编码,且在信噪比为3 dB时CNN识别器的识别准确率能够达到99%以上,而RCNN识别器在1 dB时就能够达到99%以上的识别准确率。

子载波信道模型盲均衡非合作水声正交频分复用类内调制识别

针对水声多径信道下传统正交频分复用(OFDM)类内调制识别特征稳健性不足导致识别方法失效的问题,提出了基于子载波信道模型盲均衡的水声OFDM类内调制识别方法,可识别类内调制方式包括BPSK、QPSK、8PSK及16QAM。首先,提出一种针对OFDM子载波映射符号的分块策略提高盲均衡的性能。然后,利用期望最大化(EM)算法,在该分块策略下对每个分块内的子载波映射符号进行处理,估计出信道衰落系数和噪声功率;进一步利用K均值聚类(K-means)算法得到该分块映射符号对应的信道衰落系数,并将该信道衰落系数作为EM算法的初始值,使EM算法能够快速收敛。最后,利用最大似然(ML)分类器实现OFDM类内调制识别。仿真和实验结果表明,提出的EM-Block-ML方法实现了水声多径信道下高可靠的OFDM类内调制识别,验证了所提识别方法的有效性。

基于聚类的盲信道估计算法研究与优化

正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)在现代通信系统中发挥着重要的作用,而信道估计是OFDM系统接收机的关键环节。针对传统导频信道估计算法需要大量的导频开销,降低信道带宽利用率,以及已有的盲信道估计算法估计性能差且算法复杂度高等不足,该文提出了一种基于改进聚类算法的盲信道估计算法。该算法在系统接收端把接收到信号看作一幅时频二维平面,通过使用时频窗口以迭代方式遍历该平面,在遍历过程中用改进的K-means算法对时频窗口内的符号数据做聚类分析,并根据符号先验信息以及信道时频相关性实现信道估计与均衡的目的。通过计算机仿真实验结果表明,改进K-means的盲信道估计算法的误比特率整体上低于基于简单线性预编码的盲估计算法,当信噪比大于10 dB时,改进K-means的盲信道估计算法的误比特率比子空间方法更低。在高信噪比情况下估计性能与最小均方误差(Minimum Mean Square Error,MMSE)估计算法相当。因此,相较于已有的盲信道估计算法,改进K-means的盲信道估计算法提高了估计精度,降低了算法复杂度。

一种基于stop-and-go改进的MCMA盲均衡算法

修正的常数模算法(MCMA)可以纠正相位旋转,但剩余稳态误差较大.在stop-and-go架构的基础上,提出了基于stop-and-go改进的MCMA算法.首先将局部信息量引入MCMA的误差函数,然后运用stop-and-go减少错误均衡的思想,使算法的均衡方向与MCMA保持一致,以降低剩余稳态误差.仿真结果表明,基于stop-and-go改进的MCMA盲均衡算法在保证了收敛速度的同时,码间干扰比MCMA算法降低了10dB.

Single-User Blind Channel Equalization Using Modified Constant Modulus Algorithm

A modified constant modulus algorithm (MCMA) for blind channel equalization is proposed by modifying the constant modulus error function. The MCMA is compared with the conventional constant modulus algorithm (CMA) for symbol-spaced equalization of 4PSK signals. The result shows that the performance of the MCMA is superior to that of the CMA in both convergence rate and intersymbol interference for frequency selective channels in noisy environments. Simulation results using 8PSK signals also demonstrate that a fractionally spaced equalizer can preserve performance over variations in symbol-timing phase, whereas a baud-rate equalizer cannot.

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