High-speed CMOS continuous-time complex graphic equalizer formagnetic recording

A high-speed continuous-time CMOS analog adaptive equalizer for use in magnetic recording read channels is presented. The equalizer is implemented as the summation of several bandpass filters covering different frequency bands as in a graphic equalizer. The outputs from each filter are weighted by a complex coefficient and summed, which results in a linear combiner structure guaranteed to converge under least mean square (LMS) adaptation. System-level simulations of our “complex graphic equalizer (CGE)” show that its performance is comparable to that of a ten-tap finite impulse response (FIR) equalizer following a fourth-order low-pass filter when tested with two different sequence detectors: EPR4-MLSD and fixed delay tree search with decision feedback (FDTS/DF). A five-band tunable CGE has been fabricated using a 0.8-μm CMOS technology. The highest band of the fabricated CGE was centered at 80 MHz (corresponding to channel data rate of about 200 Msymbols/s). Measured dynamic range was 68 dB, and measured total harmonic distortion was only -75 dB while consuming 97 mW at 3.3 V. The measured CGE performance agreed within 0.2 dB with the simulation results for an FDTS/DF system with an ideal CGE operating at 2.5 user bits/PW50     

Widely linear decision-feedback equalizer for time-dispersive linear MIMO channels

The paper deals with the transmission over multiple-input/multiple-output channels exhibiting time-dispersion. A minimum mean-square error equalizer based on widely linear processing combined with the decision-feedback (DF) strategy is implemented via finite-impulse-response filters. The proposed equalizer provides considerable performance gain at the expense of a limited increase in computational complexity. The performance analysis has been carried out accounting for mismatch conditions always present in practice. The results confirm the stronger sensitivity of the DF-based equalizers with respect to the feedforward-based ones when system parameters are not accurately known.     

A DECISION FEEDBACK EQUALIZER FOR NON-LINEAR CHANNELS

A Simple and useful decision feedback equalizer used for non-linear channels with severe linear distortion and mild non-linear distortion is proposed. It is a combination of a nonlinear channel equalizer based on connectionist model and a common decision feedback equalizer for linear channels. For a typical non-linear channel model it is shown that the equalization performances of the proposed equalizer are improved significantly.     

A mixed-signal decision-feedback equalizer that uses a look-aheadarchitecture

A mixed-signal decision-feedback equalizer (DFE) that uses a look-ahead architecture is described. The parallelism in the look-ahead DFE (LA DFE) achieves an increase in the data rate over a conventional DFE with a small increase in area. Fully differential analog circuits perform the convolution operation in the LA DFE, and the coefficient adaption is carried out by digital circuits. The LA DFE occupies 23 mm ² in a 2-μm CMOS process and operates at 50 Mb/s while dissipating 260 mW     

A mixed-signal RAM decision-feedback equalizer for disk drives

A mixed-signal RAM decision-feedback equalizer (DFE) that operates at 90 Mb/s is described. In the analog domain, the DFE subtracts intersymbol interference caused by the past four outputs. The equalized signal is fed into a nonuniform flash analog-to-digital converter (ADC) to produce the decision output and error signal used to adapt the RAM contents in the digital domain. With a 5 V supply voltage, the power dissipation is 260 mW during steady-state operation. The active area is 4.5 mm² in a 1 μm CMOS process     

Blind adaptive multiple-input decision-feedback equalizer with aself-optimized configuration

This paper introduces a novel blind adaptive multiple-input decision-feedback equalizer (MI-DFE) which is basically characterized by its ability to self-optimize its configuration, in terms of both structure and criteria, according to the severity of the transmission medium. In the first running mode, the novel equalizer is recursive, linear and “blindly” adapted by criteria leading to a solution closely related to the minimum MSE solution. In the second running mode, it becomes the conventional MI-DFE. From the viewpoints of both robustness and spectral efficiency, this equalizer proves to be very attractive since it avoids pathological behaviors, often encountered with the conventional trained MI-DFE, while requiring no training sequence. Furthermore, its very high speed of convergence renders it competitive in various standard applications, even in the case of burst mode transmission systems. Finally, the novel blind MI-DFE has been successfully tested on underwater acoustic communications signals, in a very severe context. The results are clearly convincing     

An adaptive analog noise-predictive decision-feedback equalizer

In this paper, an adaptive noise-predictive decision-feedback equalizer (NPDFE) is presented. The NPDFE architecture and its implementation are described. The NPDFE consists of an analog finite-impulse-response (FIR) forward equalizer, a recursive analog equalizer for noise prediction, and a decision-feedback equalizer (DFE). The recursive equalizer reduces noise enhancement and improves the signal-to-noise ratio (SNR) at the decision slicer input. The prototype targets a magnetic recording channel modeled by a Lorentzian impulse response. Measured results show that compared to a conventional DFE with FIR forward equalizer, the NPDFE achieves a SNR improvement of about 2 dB with PW₅₀=2.5T. The NPDFE consumes 130 mW at a data rate of 100 Mb/s and occupies 1.3 mm² of die area in a 0.5-μm CMOS process     

Adaptive Bayesian decision feedback equalizer for dispersive mobileradio channels

The paper investigates adaptive equalization of time-dispersive mobile radio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE     

A decision feedback equalizer with time-reversal structure

This work describes the use of a receiver with a time-reversal structure for low-complexity decision feedback equalization of slowly fading dispersive indoor radio channels. Time-reversal is done by storing each block of received signal samples in a buffer and reversing the sequential order of the signal samples in time prior to equalization. As a result, the equivalent channel impulse response as seen by the equalizer is a time-reverse of the actual channel impulse response. Selective time-reversal operation, therefore, allows a decision feedback equalizer (DFE) with a small number of forward filter taps to perform equally well for both minimum-phase and maximum-phase channel characteristics. The author evaluates the theoretical performance bounds for such a receiver and quantifies the possible performance improvement for discrete multipath channels with Rayleigh fading statistics. Two extreme cases of DFE examples are considered: an infinite-length DFE; and a DFE with a single forward filter tap. Optimum burst and symbol timing recovery is addressed and several practical schemes are suggested. Simulation results are presented. The combined use of equalization and diversity reception is considered     

Tentative chip decision-feedback equalizer for multicode wideband CDMA

We investigate a chip-level minimum mean-square-error (MMSE) decision-feedback equalizer (DFE) for the downlink receiver of multicode wideband code-division multiple-access systems over frequency-selective channels. First, the MMSE per symbol achievable by an optimal DFE is derived, assuming that all interchip interference (ICI) of the desired user can be eliminated. The MMSE of DFE is always less than or at most equal to that of linear equalizers (LE). When all the active codes belong to the desired user, the ideal DFE is able to eliminate multicode interference (MCI) and approach the performance of the single-code case at high signal-to-noise ratio (SNR) range. Second, we apply the hypothesis-feedback equalizer or tentative-chip (TC)-DFE in the multicode scenario. TC-DFE outperforms the chip-level LE, and the DFE that only feeds back the symbols already decided. The performance gain increases with SNR, but decreases with the number of active codes owned by the other users. When all the active codes are assigned to the desired user, TC-DFE asymptotically eliminates MCI and achieves single-user (or code) performance at high SNR, similarly, to the ideal DFE. The asymptotic performance of the DFE is confirmed through bit error rate simulation over various channels.     

Analog timing recovery for a noise-predictive decision-feedback equalizer

A timing recovery architecture and its CMOS implementation are described for a noise-predictive decision-feedback equalizer (NPDFE). The 0.5-/spl mu/m CMOS prototype includes timing recovery and the NPDFE and operates at 160 Mbit/s. The timing recovery blocks dissipate 27 mW from 3.3 V, occupy 0.2 mm/sup 2/, and achieve a root mean square jitter of 50 ps, which is 0.8% of a bit period.     

用于PMD补偿的判决反馈均衡器的结构研究

文章采用自适应均衡技术对偏振模色散(PMD)进行补偿,给出了判决反馈均衡器(DFE)结构,通过对前向均衡器(FFE)部分的改进,得到了适用于高速光纤通信系统的行波滤波器(TWF)及折叠级联的TWF结构,最后通过ADS仿真软件,得到了系统S参数仿真结果.     

Adaptive Bayesian equalizer with decision feedback

A Bayesian solution is derived for digital communication channel equalization with decision feedback. This is an extension of the maximum a posteriori probability symbol-decision equalizer to include decision feedback. A novel scheme utilizing decision feedback that not only improves equalization performance but also reduces computational complexity greatly is proposed. It is shown that the Bayesian equalizer has a structure equivalent to that of the radial basis function network, the latter being a one-hidden-layer artificial neural network widely used in pattern classification and many other areas of signal processing. Two adaptive approaches are developed to realize the Bayesian solution. The maximum-likelihood Viterbi algorithm and the conventional decision feedback equalizer are used as two benchmarks to asses the performance of the Bayesian decision feedback equalizer     

Dual decision feedback equalizer

The detector of Dahlman and Gudmundson (1988) ranks among the simplest known extensions of the decision feedback equalizer (DFE). The article develops a simplification of this detector, and evaluates its performance vis-a-vis other near maximum likelihood detectors. Comparable performances are observed at much lower complexity levels     

A MIMO turbo equalizer for frequency-selective channels with unknown interference

A new space-time turbo equalization algorithm is derived for frequency-selective multiple-input-multiple-output (MIMO) channels with unknown interference. The algorithm is an extension of our proposed MIMO equalization algorithm , which performs joint channel estimation, multiple users' signal detection, and decoding, all in an iterative manner. This paper's proposed algorithm uses estimates of the correlation matrix of composite unknown interference-plus-noise components to suppress the unknown interference while effectively separating multiple users' signals to be detected (referred to as "known user" later). The correlation matrix of the composite unknown interference-plus-noise components can be estimated by time averaging the instantaneous empirical correlation matrix over the training period. Since the iterative channel estimation yields better channel estimates as more iterations are performed, thereby the estimate of the correlation matrix of the unknown interference-plus-noise components also becomes more accurate. This results in better signal detection performances, even in the presence of unknown interferers. A series of computer simulations show that this paper's proposed algorithm can properly separate known users' signals while suppressing unknown interference.     

A blind decision feedback equalizer incorporating fixed lagsmoothing

A new type of blind decision feedback equalizer (DFE) incorporating fixed lag smoothing is developed in this paper. The structure is motivated by the fact that if we make full use of the dependence of the observed data on a given transmitted symbol, delayed decisions may produce better estimates of that symbol. To this end, we use a hidden Markov model (HMM) suboptimal formulation that offers a good tradeoff between computational complexity and bit error rate (BER) performance. The proposed equalizer also provides estimates of the channel coefficients and operates adaptively (so that it can adapt to a fading channel for instance) by means of an online version of the expectation-maximization (EM) algorithm. The resulting equalizer structure takes the form of a linear feedback system including a quantizer, and hence, it is easily implemented. In fact, because of its feedback structure, the proposed equalizer shows some similarities with the well-known DFE. A full theoretical analysis of the initial version of the algorithm is not available, but a characterization of a simplified version is provided. We demonstrate that compared to the zero-forcing DFE (ZF-DFE), the algorithm yields many improvements. A large range of simulations on finite impulse response (FIR) channels and on typical fading GSM channel models illustrate the potential of the proposed equalizer     

Low-complexity adaptive decision-feedback equalization of MIMO channels

A new adaptive MIMO channel equalizer is proposed based on adaptive generalized decision-feedback equalization and ordered-successive interference cancellation. The proposed equalizer comprises equal-length subequalizers, enabling any adaptive filtering algorithm to be employed for coefficient updates. A recently proposed computationally efficient recursive least squares algorithm based on dichotomous coordinate descents is utilized to solve the normal equations associated with the adaptation of the new equalizer. Convergence of the proposed algorithm is examined analytically and simulations show that the proposed equalizer is superior to the previously proposed adaptive MIMO channel equalizers by providing both enhanced bit error rate performance and reduced computational complexity. Furthermore, the proposed algorithm exhibits stable numerical behavior and can deliver a trade-off between performance and complexity.     

A NEW ADAPTIVE EQUALIZER STRUCTURE FOR NONLINEAR CHANNELS

Based on the analysis of nonlinear channel models,a new connectionist model ofadaptive equalizer is constructed.Comparing with the connectionist model using the Volterraseries to extend the input vector space,the number of weights with the new structure is reducedsignificantly.It is shown by simulations that the weight values of the new scheme converge to theoptimal values closely for non-minimum phase channels as well minimum phase channels,if thechannel noise is small enough.Testing results of the BER(Bit Error Rate)tell us that the newadaptive equalizer for nonlinear channels is superior to the conventional linear equalizers in theequalization performances.     

A binary adaptive decision-selection equalizer for channels with nonlinear intersymbol interference

An enhanced adaptive decision feedback equalizer (ADFE) is presented for binary data transmission applications where the communication channel exhibits nonlinear intersymbol interference (ISI). The nonlinearity in the channel manifests itself as a distorted constellation space constructed from the equalizer input state variables. Since a conventional ADFE can construct a hyperplane decision boundary of only one orientation with symmetrically spaced distance from the origin as a function of the detected feedback symbols and feedback filter coefficient values, there is room for improvement since the distorted constellation of the nonlinear system is better served by hyperplane boundaries of varying orientation. The method proposed here is not to feed back the decision variables but, instead, to use these binary variables to choose and adapt different sets of coefficients, i.e., different hyperplane boundaries. Hence, the name given to this new method is the adaptive decision-selection equalizer (ADSE). Although the hyperplane may not be the optimum boundary for the conditional constellations, in many cases, it is an adequate approximation. Nonetheless, for nonlinear channels, the ADSE is generally an improvement over the conventional ADFE in high signal-to-noise ratio (SNR) regimes, where the bit error rate (BER) is within the desired operating range. The major advantage of the new method is improved performance on the studied channel while retaining simplicity when implemented as a variation of the least-mean-squared (LMS) algorithm. Some drawbacks are decreased convergence rate and limitations of the minimum mean-squared-error (MMSE) strategy of optimization, as implemented by the LMS algorithm, for a system where error probability, not MMSE, is important.