Renato R. Lopes

The soft-feedback equalizer for turbo equalization of highly dispersive channels

The complexity of a turbo equalizer based on the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm is manageable only for mildly dispersive channels having a small amount of memory. To enable turbo equalization of highly dispersive channels, we propose the soft-feedback equalizer(SFE). The SFE combines linear equalization and soft intersymbol-interference cancellation. Its coefficients are chosen to minimize the mean-squared error(MSE) between the equalizer output and the transmitted sequence, under a Gaussian approximation to the a priori information and the SFE output. The resulting complexity grows only linearly with the number of coefficients, as opposed to the quadratic complexity of previously reported minimum-MSE structures. We will see that an SFE-based turbo equalizer consistently outperforms another structure of similar complexity, and can outperform a BCJR-based scheme when complexity is taken into account.

Approximate minimum bit-error rate multiuser detection

The minimum mean-squared-error (MMSE) linear multiuser detector is popular because of its good performance and amenability to adaptive implementation. However, there are circumstances in which the linear detector that minimizes the bit-error rate (BER) can significantly outperform the MMSE detector. We propose a low-complexity adaptive algorithm for approximating the minimum BER linear multiuser detector.

Blind iterative channel identification and equalization

We propose an iterative solution to the problem of blindly and jointly identifying the channel response and transmitted symbols in a digital communications system. The proposed algorithm iterates between a symbol estimator, which uses tentative channel estimates to provide soft symbol estimates, and a channel estimator, which uses the symbol estimates to improve the channel estimates. The proposed algorithm shares some similarities with the expectation-maximization (EM) algorithm but with lower complexity and better convergence properties. Specifically, the complexity of the proposed scheme is linear in the memory of the equalizer, and it avoids most of the local maxima that trap the EM algorithm.

Unsupervised Processing of Geophysical Signals: A Review of Some Key Aspects of Blind Deconvolution and Blind Source Separation

This article reviews some key aspects of two important branches in unsupervised signal processing: blind deconvolution and blind source separation (BSS). It also gives an overview of their potential application in seismic processing, with an emphasis on seismic deconvolution. Finally, it presents illustrative results of the application, on both synthetic and real data, of a method for seismic deconvolution that combines techniques of blind deconvolution and blind source separation. Our implementation of this method contains some improvements overthe original method in the literature described.

Exploiting error-control coding in blind channel estimation

Despite the widespread use of forward-error control (FEC) coding, most channel estimation techniques ignore its presence, and instead make the simplifying assumption that the transmitted symbols are uncoded. However, FEC induces structure in the transmitted sequence that can be exploited to improve channel estimates. Furthermore, soft-output decoding can improve decision-driven techniques. We propose a technique for exploiting FEC in channel estimation that combines iterative channel estimation with turbo equalization. We present one example showing that an estimator that exploits FEC can attain the same accuracy as one that ignores FEC, but with an SNR that is 6 dB lower.

Modified Kalman Filters for Channel Estimation in Orthogonal Space-Time Coded Systems

We derive and analyze two modified Kalman channel estimators (KCE) for time-varying, flat, spatially correlated MIMO channels in systems employing orthogonal space-time block codes: the steady-state KCE, which is less complex than the KCE, and the fading memory KCE, which is more robust to model mismatch.

Hybrid MIMO Transceiver Scheme with Antenna Allocation and Partial CSI at Transmitter Side

Hybrid multiple-input multiple-output (MIMO) transceiver scheme (HMTS) combines transmit diversity and spatial multiplexing, thus achieving at the same time the two possible spatial gains offered by MIMO channels. In the design of HMTS spatial diversity and spatial multiplexing branches are disposed in parallel in order to achieve diversity and multiplexing gains at the same time. Since the spatial multiplexing branches have no protection, they are more susceptible to the fading effect becoming the bottleneck in the performance of the whole transceiver. In this paper, we propose a solution to this bottleneck in the hybrid MIMO transceiver scheme using a partial channel state information at the transmitter side. The idea is to perform an antenna allocation. Thus, the most powerful subchannels are allocated to the most susceptible layers (spatial multiplexing branches). Through this solution we decrease the performance imbalance between the two layers of the HMTS G3+1, increasing the whole transceiver performance with low complexity feedback requirements

Soft-output decision-feedback equalization with a priori information

Soft-output equalizers that exploit a priori information on the channel inputs play a central role in turbo equalization. Such equalizers are traditionally implemented with the forward-backward or BCJR algorithm, whose complexity is prohibitive for channels with large memory. Many reduced-complexity alternatives to the BCJR algorithm have been proposed that use a linear equalizer and use the a priori information to perform soft intersymbol interference cancellation. In this work, we propose a soft-feedback equalizer (SFE) that combines the equalizer output and the a priori information to improve interference cancellation. Also, by assuming a statistical model for the a priori information and the SFE output, we obtain an equalizer with linear complexity, as opposed to the quadratic complexity of some similar structures. Simulation results show that the SFE may perform within 1 dB of a system based on an BCJR equalizer, within 0.3 dB of quadratic complexity schemes, and consistently outperforms other linear complexity schemes.

Reduced-complexity turbo equalization for high-density magnetic recording systems

This paper considers a decision-feedback-equalizer (DFE)-based soft decision detector as an alternative for the Bahl-Cocke-Jelinek-Raviv (BCJR) front-end for a coded magnetic recording channel. In previous work, a bidirectional-arbitrated DFE (BAD) was shown to perform in between a BCJR detector and a minimum mean-square error DFE. We propose a soft-output BAD (S-BAD) which takes advantage of the original structure and is suitable for iterative decoding when used with outer codes. We show that with a convolutional code as an outer code, S-BAD performs close to higher complexity detectors.

Implementation aspects of eigendecomposition-based high-resolution velocity spectra

In this paper, we discuss high-resolution coherence functions for the estimation of the stacking parameters in seismic signal processing. We focus on the Multiple Signal Classification which uses the eigendecomposition of the seismic data to measure the coherence along stacking curves. This algorithm can outperform the traditional semblance in cases of close or interfering reflections, generating a sharper velocity spectrum. Our main contribution is to propose complexity-reducing strategies for its implementation to make it a feasible alternative to semblance. First, we show how to compute the multiple signal classification spectrum based on the eigendecomposition of the temporal correlation matrix of the seismic data. This matrix has a lower order than the spatial correlation used by other methods, so computing its eigendecomposition is simpler. Then we show how to compute its coherence measure in terms of the signal subspace of seismic data. This further reduces the computational cost as we now have to compute fewer eigenvectors than those required by the noise subspace currently used in the literature. Furthermore, we show how these eigenvectors can be computed with the low-complexity power method. As a result of these simplifications, we show that the complexity of computing the multiple signal classification velocity spectrum is only about three times greater than semblance. Also, we propose a new normalization function to deal with the high dynamic range of the velocity spectrum. Numerical examples with synthetic and real seismic data indicate that the proposed approach provides stacking parameters with better resolution than conventional semblance, at an affordable computational cost.