Xavier Wautelet

MMSE-based fractional turbo receiver for space-time BICM over frequency-selective MIMO fading channels

Space-time bit-interleaved coded multilevel modulation over frequency-selective multiple-input multiple-output (MIMO) fading channels is considered. The receiver is turbo based. A soft-input soft-output (SISO) space-time fractionally spaced equalizer is designed for the minimum mean square error (MMSE) criterion. Simulation results show the ability of the receiver to account for intersymbol interference, co-antenna interference, and its capability to exploit transmit, multipath, and receive diversity. These results are also compared to a bit-error-rate lower bound.

Comparison of EM-Based Algorithms for MIMO Channel Estimation

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

MMSE turbo receiver for space-frequency bit-interleaved coded OFDM

This paper considers transmission of OFDM modulated signals over MIMO frequency-selective fading channels. To benefit from diversity, coding is introduced in the space and frequency dimensions by means of bit-interleaved coded modulation. Assuming the OFDM cyclic extension works properly, MIMO space equalization has to be performed for each individual tone. A set of low-complexity MMSE SISO equalizers, achieving both spatial-interference mitigation and demodulation, is used. This set of MMSE equalizers exchanges soft information with the SISO decoder according to the turbo principle. The performance of this scheme is illustrated by computer simulations for quasistatic frequency-selective channels. It is shown that this receiver is able to exploit space and frequency diversity.

Soft Estimation of Time-Varying Frequency Selective Channels Using Kalman Smoothing

This paper addresses soft estimation of time-varying frequency selective channels using Kalman smoothing. The proposed estimator uses soft extrinsic information provided by a channel decoder. It is intended to improve the performance of an already existing Kalman filtering-based estimator by exploiting all - rather than part of - the data at the receiver disposal. It is the linear estimator exhibiting for the case of interest the minimum mean-squared estimation error. Its complexity is shown to be quite low. An approximated analytical calculation of the mean- squared estimation error (MSEE), both for Kalman filtering and Kalman smoothing, is also proposed. Simulation results illustrate the performance gain of smoothing over filtering and validate our calculation of the MSEE.

Frame-Error-Rate-wise Optimal Code-Aided Hypothesis Testing

This paper addresses the issue of code-aided hypothesis testing in communication systems, i.e., the estimation of discrete-valued nuisance parameters. A hypothesis testing procedure optimal in the sense of the minimization of the frame-error rate (FER) is derived. Its complexity is shown to be comparable to other recently-proposed code-aided hypothesis procedures. Moreover, when a conditional maximum a posteriori decision rule is used to make the decisions about the transmitted sequence, it is shown that the proposed hypothesis testing procedure combined with sequence detection reduces, in a good approximation, to a joint maximum-likelihood problem. Finally, as an illustrative example, we show the case of phase ambiguity resolution for a convolutionally-coded transmission.

Cramer Rao bounds for channel estimation with symbol a priori information

In this paper we derive the Cramer-Rao bound (CRB) for the estimation of a multiple-input multiple-output (MIMO) channel when a given a priori information about the transmitted sequence is available. This approach also enables to compute the non-data-aided (NDA) bound, i.e. the CRB when the receiver only knows the symbol constellation. We explore various scenarios, including the case of a deterministic channel and the case of a random channel. Numerical evaluations of the CRB-based bounds are presented for different channels. In particular, the influence of the symbol-constellation size is highlighted. Finally, the performance of an estimator based on the expectation-maximization algorithm is shown to be very close to the proposed bounds.

Iterative Synchronization: EM Algorithm Versus Newton-Raphson Method

This paper deals with iterative maximum-likelihood synchronization of a scalar parameter. An efficient implementation of the Newton-Raphson (NR) maximum-search method is proposed. Considering the latter implementation, the NR approach is shown to be an attractive alternative to synchronization methods based on the expectation-maximization (EM) algorithm. Simulation results for the case of phase-offset synchronization show that NR method usually increases the speed of convergence of the synchronization algorithm

Advanced Transmitter and Receiver Design

This chapter addresses the design of receivers for multiple-input multiple-output (MIMO) communication. It covers the decoding of error-correcting codes, the mitigation of intersymbol and cross-antenna interference, and the synchronization, that is, the estimation of channel parameters. It presents the techniques that are based on the turbo principle. The turbo principle originates from the turbo decoding algorithm, which has shown how to approximate an intractable optimal decoder with a simpler, iterative decoder. It also presents the receivers that use the same design idea, referred to as the turbo principle, to carry out interference mitigation and synchronization. This results in high-performance iterative receivers that address decoding globally by iterating between tasks, and that approximate closely the optimal but intractable decoders. It introduces turbo equalization, a technique to mitigate intersymbol interference and extends turbo equalization to MIMO channels, where the interference comes both from adjacent symbols and from other antennas.

Calculating the Performance of a Soft-Information-Based Best Linear Unbiased Estimator of Amplitude and Carrier Phase Offset

This paper analytically calculates the expectation and the variance of a soft-information-based best linear unbiased estimator of amplitude and carrier phase offset. Long data frames are considered. The calculation includes the impact on the performance of the presence of training symbols as well as non-Gaussianity of the log-likelihood ratios (LLRs) fed to the estimator input. It is also analyzed how the properties of the estimator are affected when the ratio between the mean and variance of the LLRs is not equal to 1/2

Turbo-equalization considering bit-interleaved turbo-coded modulation: performance bounds

The goal of this paper is to assess the performance of turbo-equalization (TE), considering at the transmitter bit-interleaved coded modulation (BICM) with a turbo-code as error-correcting code. The bounding techniques that we have recently proposed in Dejonghe et al. (2004) are extended to this particular problem. Simulation results show the relevance of the proposed bounds. By the way, this leads to analytical tools for the understanding and optimization of such turbo-systems.