Blind equalization/detection for OFDM signals overfrequency-selective channels

A novel equalization/detection algorithm for orthogonal frequency division multiplexing (OFDM) signals transmitted over frequency-selective channels is introduced and investigated. The algorithm stems from the recognition that the Fourier transform processing inherent in OFDM turns a single wideband frequency-selective channel into a set of correlated narrowband frequency-flat fading channels. This suggests that sequence detection techniques, such as those discussed by Vitetta et al. (see IEEE Trans. Commun., vol.43, p.2750-8, 1995, IEEE Trans. Commun., vol.43, pt.II, p.1256-9, 1995, and Proc. IEEE Commun. Theory Mini-Conf (Globecom '96), London, UK, p.153-7, 1996), for time-selective flat-fading channels, can be also profitably utilized for joint equalization and decoding of OFDM signals in the frequency domain. Simulation results show that the proposed detection strategy, implemented via a standard Viterbi algorithm, provides improved performance over differential detection, with a moderate increase in receiver complexity and without requiring the periodic transmission of training blocks     

基于子空间的无线光通信副载波盲均衡算法研究

文中基于Gamma-Gamma光强起伏分布大气湍流信道模型,对无线光副载波相移键控调制系统进行了子空间盲均衡算法的研究。对比分析了经过大气湍流信道盲均衡前后的副载波调制信号星座图聚敛性,给出了均衡后不同光强起伏方差下的误码率曲线,当R2=0.1,信噪比为20 dB时,误码率从4.710-1降低到1.5610-3,均衡后误码率明显改善。同时采用两种典型实测天气数据(阴、中雨)模拟大气信道进行了子空间盲均衡实验,均衡后调制信号星座图聚敛性与相位可识别性明显优于均衡前。仿真结果表明,子空间盲均衡算法对大气湍流信道下的副载波调制信号有良好的均衡效果。     

Blind channel equalization using chirp modulating signals

This paper addresses the problem of blind channel equalization in the context of digital communications. Recent results have shown that certain operations applied to the source signal at the transmitter help in the blind identification and equalization of the channel at the receiver. In this paper, the baseband data signal is multiplied with a chirp sequence. Exploiting certain structural properties arising from this operation, a batch-type algorithm is obtained for calculating the equalizer's coefficients. Conditions on the chirp sequence parameters are obtained that guarantee an equalization solution. A low-complexity adaptive algorithm is also proposed. Finally, extensive simulations, and comparisons with other well-known blind techniques, illustrate the excellent performance of this algorithm.     

Blind equalization algorithm based on complex support vector regression

A new blind equalization algorithm for complex valued signals was proposed based on the framework of complex support vector regression(CSVR).In the proposed algorithm,the error function of multi-modulus algorithm (MMA) was substituted into CSVR to construct the cost function,and the regression relationship was established by widely linear estimation,and the equalizer coefficients were determined by the iterative re-weighted least square (IRWLS) method.Different from spliting the complex valued signals into real valued signals used in support vector regression,the Wirtinger’s calculus was used in complex support vector regression to analyze the complex signals directly in the complex regenerative kernel Hilbert space.Simulation experiments show that for QPSK modulated signals,compared with the blind equalization algorithm based on support vector regression,the equalization performance of the proposed algorithm is significantly improved in linear channel and nonlinear channel by choosing appropriate kernel function and iterative optimization method.     

Classification of LPI radar signals using spectral correlation and support vector machines

In modern radar systems, low probability of intercept (LPI) waveforms are used to make detection by a potential adversary difficult. This is accomplished using wideband waveforms, frequency hopping, and continuous waveforms (FMCW) to reduce the signal profile. The low signal profile of the LPI signal enables the radar to perform detection and or target tracking while the target remains unaware. Several modulation techniques such as polytime codes, polyphase codes, FSK, and FMCW are used to produce LPI signals for transmission. This paper looks at the ability of spectral correlation along with a support vector machine in order to automatically classify the different LPI signal types in a non-cooperative environment.     

Blind equalization for short burst wireless communications

In this paper, we propose a dual mode blind equalizer based on the constant modulus algorithm (CMA). The blind equalizer is devised for short burst transmission formats used in many current wireless TDMA systems as well as future wireless packet data systems. Blind equalization is useful for such short burst formats, since the overhead associated with training can be significant when only a small number of bits are transmitted at a time. The proposed equalizer overcomes the common problems associated with classic blind algorithms, i.e., slow convergence and ill-convergence, which are detrimental to applying blind equalization to short burst formats. Thus, it can eliminate the overhead associated with training sequences. Also, the blind equalizer is extended to a two branch diversity combining blind equalizer. A new initialization for fractionally spaced CMA equalizers is introduced. This greatly improves the symbol timing recovery performance of fractionally spaced CMA equalizers with or without diversity, when applied to short bursts. Through simulations with quasi-static or time-varying frequency selective wireless channels, the performance of the proposed equalizer is compared to selection diversity and conventional equalizers with training sequences. The results indicate that its performance is far superior to that of selection diversity alone and comparable to the performance of equalizers with short training sequences. Thus, training overhead can be removed with no performance degradation for fast time-varying channels, and with slight performance degradation for static channels     

Blind equalization of constant modulus signals using an adaptiveobserver approach

The paper addresses the problem of blind equalization of constant modulus signals which are degraded by frequency selective multipath propagation and additive white noise. An adaptive observer is used to update the weights of an FIR equalizer in order to restore the signal's constant modulus property. The observer gain is selected using fake algebraic Riccati methods in order to guarantee local stability. The performance of this method is compared to the constant modulus algorithm for simulated FM-FDM signals and exhibits significantly better convergence properties, particularly for heavy-tailed noise     

Novel semi-blind ICI equalization algorithm for wireless OFDM systems

Intercarrier interference is deemed as one of the crucial problems in the wireless orthogonal frequency division multiplexing (OFDM) systems. The conventional ICI mitigation schemes involve the frequency-domain channel estimation or the additional coding, both of which require the spectral overhead and hence lead to the significant throughput reduction. Besides, the OFDM receivers using the ICI estimation rely on a large-dimensional matrix inverter with high computational complexity especially for many subcarriers such as digital video broadcasting (DVB) systems and wireless metropolitan-area networks (WMAN). To the best of our knowledge, no semi-blind ICI equalization has been addressed in the existing literature. Thus, in this paper, we propose a novel semi-blind ICI equalization scheme using the joint multiple matrix diagonalization (JMMD) algorithm to greatly reduce the intercarrier interference in OFDM. However, the well-known phase and permutation indeterminacies emerge in all blind equalization schemes. Hence we also design a few OFDM pilot blocks and propose an iterative identification method to determine the corresponding phase and permutation variants in our semi-blind scheme. Our semi-blind ICI equalization algorithm integrating the JMMD with the additional pilot-based iterative identification is very promising for the future high-throughput OFDM systems. Through Monte Carlo simulations, the QPSK-OFDM system with our proposed semi-blind ICI equalizer can achieve significantly better performance with symbol error rate reduction in several orders-of-magnitude. For the 16QAM-OFDM system, our scheme can also improve the performance over the plain OFDM system to some extent.     

认知无线电中OFDM信号信噪比盲估计

针对认知正交频分复用(OFDM,orthogonal frequency division multiplexing)系统中低信噪比多径信道下传统的OFDM信号信噪比盲估计算法的估计性能差,计算复杂度高的问题,提出一种新的OFDM信号信噪比盲估计方法,该方法首先利用自相关函数的特性粗略估计出信道阶数,确定循环前缀部分中不受符号间干扰的数据区间,然后根据选定区间的数据的自相关函数值估计接收信号的信号功率,最后利用循环前缀数据为部分有用数据的复制这一特性估计出噪声功率,从而估计出接收信号的信噪比。仿真实验结果表明,提出的方法无需任何先验信息,在低信噪比多径信道下具有良好的估计性能,且计算复杂度低,更适合于认知OFDM系统。     

Blind detection of equalization errors in communication systems

In adaptive channel equalization, transmitted symbol estimates at the equalizer output may be in error because of excessive channel noise, convergence of the equalizer to a “closed-eye” local minimum, or error propagation if the equalizer has a decision feedback structure. This paper is concerned with the detection of equalization errors (i.e., errors in transmitted symbol estimates) in a blindfolded manner whereby no direct access to the channel input is required. The detection problem is cast into a binary hypothesis testing framework. Assuming a linear communication channel that is time-invariant during the test interval, a relationship between the presence of equalization errors and time variations in the underlying linear model taking the transmitted symbol estimates to the equalizer input is established. Based on this relationship, a uniformly most powerful test is constructed to detect the presence of equalization errors in finite-length observations. Finite sample size and asymptotic detection performance of the test is studied. A method for estimating the equalization delay without direct access to the channel input is developed. The effectiveness of the test is illustrated by way of computer simulations     

Decision feedback equaliser design using support vector machines

The conventional decision feedback equaliser (DFE) that employs a linear combination of channel observations and past decisions is considered. The design of this class of DFE is to construct a hyperplane that separates the different signal classes. It is well known that the popular minimum mean square error (MMSE) design is generally not the optimal minimum bit error rate (MBER) solution. A strategy is proposed for designing the DFE based on support vector machines (SVMs). The SVM design achieves asymptotically the MBER solution and is superior in performance to the usual MMSE solution. Unlike the exact MBER solution, this SVM solution can be computed very efficiently     

Adaptive equalization for high-speed indoor wireless data communication

The performance of a 4-QAM indoor wireless data communication system with adaptive equalizer is investigated. The effectiveness of using linear and decision-feedback equalizer for Rayleigh and Rician frequency-selective indoor channels is evaluated, and contrasted to the performance of a 4-QAM modem without equalizer. The effects of some important channel and system parameters (multipath spreads up to 200 ns, data rates up to 25 Mbit/s, signaling pulse rolloff factor between 0.5 and 1.0, and additive, white Gaussian noise) on the indoor communication system performance are examined and presented in the paper. The indoor propagation measurements, carried out in a research laboratory, provided data to be used for BER performance assessments of the system with and without equalizer. The performance results based on computer generated channels are then compared with those obtained for measured channel impulse responses.     

Alzheimer's diagnosis using eigenbrains and support vector machines

An accurate and early diagnosis of the Alzheimer's disease (AD) is of fundamental importance for the patient's medical treatment. Single photon emission computed tomography (SPECT) images are commonly used by physicians to assist the diagnosis. Presented is a computer-assisted diagnosis tool based in a principal component analysis (PCA) dimensional reduction of the feature space approach and a support vector machine (SVM) classification method for improving the AD diagnosis accuracy by means of SPECT images. The most relevant image features were selected under a PCA compression, which diagonalises the covariance matrix, and the extracted information was used to train an SVM classifier, which could classify new subjects in an unsupervised manner.     

Estimation of highly selective channels for OFDM system by complex least squares support vector machines

A channel estimator using complex least squares support vector machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long Term Evolution (LTE) downlink. This channel estimation algorithm use knowledge of the pilot signals to estimate the total frequency response of the channel. Thus, the algorithm maps trained data into a high dimensional feature space and uses the structural risk minimization (SRM) principle, which minimizes an upper bound on the generalization error, to carry out the regression estimation for the frequency response function of the highly selective channel. Simulation results show that the proposed method has better performance compared to the conventional LS and Decision Feedback methods and it is more robust at high speed mobility.     

Covering numbers for support vector machines

Support vector (SV) machines are linear classifiers that use the maximum margin hyperplane in a feature space defined by a kernel function. Previously, the only bounds on the generalization performance of SV machines (within Valiant's probably approximately correct framework) took no account of the kernel used except in its effect on the margin and radius. It has been shown that one can bound the relevant covering numbers using tools from functional analysis. In this paper, we show that the resulting bound can be greatly simplified. The new bound involves the eigenvalues of the integral operator induced by the kernel. It shows that the effective dimension depends on the rate of decay of these eigenvalues. We present an explicit calculation of covering numbers for an SV machine using a Gaussian kernel, which is significantly better than that implied by previous results     

Supervised classification of marble textures using support vector machines

A novel approach is described for the supervised classification of marble textures in different classes according to visual appearance, using sum and difference histograms for texture analysis and feature extraction, and support vector machines for classification. Results show very good discrimination between classes.     

Linear and decision-feedback per tone equalization for DMT-based transmission over IIR channels

The per-tone equalizer (PTEQ) has been presented as an attractive alternative for the classical time-domain equalizer (TEQ) in discrete multitone (DMT) based systems, such as ADSL systems. The PTEQ is based on a linear minimum mean-square-error (L-MMSE) equalizer design for each separate tone. In this paper, we reconsider DMT modulation and equalization in the ADSL context under the realistic assumption of an infinite impulse response (IIR) model for the wireline channel. First, optimum linear zero-forcing (L-ZF) block equalizers for arbitrary IIR model orders and cyclic prefix (CP) lengths are developed. It is shown that these L-ZF block equalizers can be decoupled per tone, hence they lead to an L-ZF PTEQ. Then, based on the L-ZF PTEQ, low-complexity L-MMSE PTEQ extensions are developed: the linear PTEQ extension exploits frequency-domain transmit redundancy from pilot and unused tones; alternatively, a closely related decision-feedback PTEQ extension can be applied. The PTEQ extensions then add flexibility to a DMT-based system design: the CP overhead can be reduced by exploiting frequency-domain transmit redundancy instead, so that a similar bitrate as with the original PTEQ is achieved at a lower memory and computational cost or, alternatively, a higher bitrate is achieved without a considerable cost increase. Both PTEQ extensions are also shown to improve the receiver's robustness to narrow-band interference.     

Computational load reduction in decision functions using support vector machines

A new method of reducing the computational load in decision functions provided by a support vector classification machine is studied. The method exploits the geometrical relations when the kernels used are based on distances to obtain bounds of the remaining decision function and avoids to continue calculating kernel operations when there is no chance to change the decision. The method proposed achieves savings in operations of 25–90% whilst keeping the same accuracy. Although the method is explained for support vector machines, it can be applied to any kernel binary classifier that provides a similar evaluation function.     

Improved CMA-FSE blind equalization algorithm for wireless ultraviolet communication

There are serious intersymbol interference (ISI) and signal attenuation in wireless ultraviolet communication system.Aiming at this problem,an improved constant modulus fractionally spaced equalizer (CMA-FSE) based on signal-to-noise (SNR) estimation was proposed.The algorithm combined the fractionally spaced equalizer (FSE) and constant modulus algorithm (CMA) for blind equalization of wireless ultraviolet channels.The input SNR was measured by the mean square value of the received signal,and it was used to determine the best iterative step to ensure the convergence of the equalization algorithm.Simulation results show that the improved CMA-FSE algorithm can converge rapidly under various SNR,and it can effectively suppress ISI and improve the BER performance of the system.Compared with the existing algorithms,the improved algorithm is more useful in channel tracking and noise suppression.     

Image tamper detection and classification using support vector machines

The use of robust watermarks for attack characterisation is an area of considerable potential which has been largely overlooked to date. The authors extend their earlier work on accurate attack characterisation using a double watermarking technique to include a larger library of attacks. It is shown that the complexity of the double watermarking technique can be reduced with only a very small performance penalty. A further reduction in the algorithm complexity can be achieved by removing the thresholding process from the watermark estimation procedure. Analysis of the nature and location of the characterisation errors for the above methods is also presented.