We investigate turbo equalization, or iterative equalization and decoding, as a receiver technology for systems where data is protected by an error-correcting code, shuffled by an interleaver, and mapped onto a signal constellation for transmission over a frequency-selective channel with unknown time-varying channel impulse response. The focus is the concept of soft iterative channel estimation, which is to improve the channel estimate over the iterations by using soft information fed back from the decoder from the previous iteration to generate "extended training sequences" between the actual transmitted training sequences.

Iterative channel estimation for turbo equalization of time-varying frequency-selective channels

Iterative channel estimation can improve the channel-state information (CSI) with respect to noniterative estimation. New iterative channel estimators based on the expectation-maximization (EM) algorithm are proposed in this paper. A first estimator, called the unbiased EM (UEM), is designed to unbias the EM estimates. A second estimator is then put forward, which is based on the expectation-conditional-maximization (ECM) algorithm, and its complexity is lower than that of the EM. An unbiased ECM (UECM) estimator is also proposed. Although the unbiasedness of the UEM and UECM estimators is not rigorously proved, the use of these names is explained in the paper. The new estimators are compared with well-known ones, such as the EM, the decision-directed (DD), and the data-aided (DA) estimators. Simulations are reported for a turbo receiver operating over frequency-selective multiple-input multiple-output channels. It is shown that the UEM channel estimator outperforms the EM, and that the ECM-based estimators are very close to the EM-based ones

Comparison of EM-Based Algorithms for MIMO Channel Estimation

Channel estimation schemes suitable for interleave-division multiple access (IDMA) systems are presented. Training and data are superimposed. Training-based and semiblind linear channel estimators are derived and their performance is discussed and compared. Monte Carlo simulation results are presented showing that the derived channel estimators in conjunction with a superimposed pilot sequence and chip-by-chip processing are able to track fast-fading frequency-selective channels. As opposed to conventional channel estimation techniques, the BER performance even improves with increasing Doppler spread for typical system parameters. An error performance close to the case of perfect channel knowledge can be achieved with high power efficiency.

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Iterative pilot-layer aided channel estimation with emphasis on interleave-division multiple access systems

In this paper, we propose a soft-iterative approach for the estimation of block-fading channels based on multiblock (MB) processing. Coded transmission over fading channels is usually performed by interleaving the code word over a large time interval and arranging it into data packets (or blocks) that include also pilot symbols to allow the estimation of the time-varying channel. In this paper, we propose to exploit this block-based structure, and also the inherent latency of soft-iterative receivers due to data interleaving, to improve the channel estimate accuracy. Within the observed frame of blocks, the MB approach uses the invariance of some multipath features, such as delays and mean powers, to estimate the covariance matrix of the channel vector and to identify the corresponding channel subspace. Here, the MB method is extended to incorporate soft information, which is available in iterative receiver structures. The performance of this soft-iterative method is derived in terms of mean square error of the estimate by using a general framework that applies also to other soft-based methods in the literature. A performance comparison is carried out among these estimation methods, both analytically and by numerical simulations, showing the benefits of the proposed approach on the turbo equalizer convergence

Soft-Iterative Channel Estimation: Methods and Performance Analysis

Recursive-least-square (RLS) algorithm is widely used in many areas with real-time implementation using digital signal processors. In this paper, the authors present a pure hardware implementation of a self-tuning regulator (STR) that uses a real-time RLS algorithm as the parameter estimator. The STR contains a controller design circuit and a controller circuit. Due to RLS computation-precision and dynamic-range requirements, the hardware implementation uses a floating-point format. The floating-point processing elements presented in this paper use parameterized design, where the number of exponents and mantissa bits can be changed as the data range and the accuracy of a specific application require. The strategies for overcoming the covariance matrix asymmetrical problem during the hardware computation and the covariance matrix resetting is introduced when the system is poorly exciting are presented. The design was verified with real-time experiments using a new testbed. The experiment results are presented.

A floating-point FPGA-based self-tuning regulator

To combat the effect of intersymbol interference (ISI) while transmitting data over an ISI channel in a coded data transmission system, the impulse response of the channel is required. As part of the turbo equalization approach, which facilitates iterative equalization and decoding, we introduce a method to iteratively improve the quality of the estimate of the channel characteristics. This is done by incorporating soft information fed back by the decoder to improve the initial estimate, obtained for example using a training sequence. Decision criteria based on the analytical calculation of the variance of the channel estimation error are derived to decide whether the soft information improves the quality of the estimate. The considered estimation algorithm is the well-known recursive-least-squares algorithm. It turns out that incorporating soft information for iterative channel estimation does not always improve the quality of the estimate. If it does, the bit-error-rate performance improves significantly over a system not using soft iterative channel estimation.

Performance of soft iterative channel estimation in turbo equalization

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Performance of soft iterative channel estimation in turbo equalization