Decision directed channel estimation (DD-CE) techniques turn useful for transmissions in time-varying wireless vehicular channels. CE has to be updated several times during the transmission of long data frames. Related works assume that, for IEEE802.11p-compliant physical layer systems, DDCE is performed for every input OFDM symbol, hence independently of the experienced Doppler effect. The purpose of this paper is twofold. First, it is shown how the estimation of the Doppler spread can be reliably performed in vehicular scenarios, and, moreover, an improved yet simplified version of the hybrid autocorrelation-based estimator is proposed. Second, it is discussed how the resulting vehicle relative speed estimate can be used to tune the CE update refresh period accordingly (and thus reduce the power consumption and processing time at the receiver for low to medium speed ranges). In order to reach this goal, it is introduced a novel method that relates the CE update rate to a maximum pre-established threshold value of the signal-to-noise ratio degradation due to CE update delay.

The robustness of the linear minimum mean square error (LMMSE) channel estimator is studied with respect to the reliability of the estimated channel correlation matrix used for its implementation. The analysis is of interest in practical applications of multiple-input multiple-output (MIMO) systems, where a perfect estimate of the channel correlation matrix is not available. The channel estimation mean square error (MSE) is analytically analyzed assuming a general structure for the estimated channel correlation matrix used to implement the LMMSE channel estimator. The obtained results are successively detailed to the case of channel correlation matrices derived by sample correlation estimation methods. It is observed that the use of a coarse estimate of the channel correlation matrix can lead to a severe degradation on the LMMSE channel estimator performance, whereas the simpler least-square (LS) channel estimator may provide comparatively better results. Nevertheless, it is shown that a robust approach, although suboptimal, relies on implementing the LMMSE channel estimator by assuming transmissions over uncorrelated channels, since, with such an assumption, the resulting estimation MSE is certainly smaller than for the LS channel estimator.

In this paper, we investigate the effects of imperfect knowledge of the channel covariance matrix on the performance of a linear minimum mean-square-error (MMSE) estimator for multiple-input multiple-output (MIMO) channels. The estimation mean-square-error (MSE) is analytically analyzed by providing both a very tight lower bound and an upper bound. The proposed analysis is useful for the understanding of how estimation accuracy of the channel covariance matrix impacts on system performance, depending on the average signal-to-noise ratio (SNR) and specific propagation conditions. Conclusions are fully supported by numerical results.

This paper deals with the impact of channel estimation errors on the capacity of correlated fading MIMO channels. In literature the effect of channel uncertainty at the receiver is often investigated with the assumption that channel estimation errors are identically distributed and independent of both SNR and channel correlation values. In this paper we extend the analysis by considering correlation among the channel estimation errors as typically happen when the MIMO channel to be estimated is spatially correlated. We propose and study upper and lower bounds on the MIMO channel capacity giving emphasis to the impact of the resulting channel estimation error, through its covariance matrix. The role played by the effective SNR and spatial correlation is studied by detailing the results to MIMO systems employing linear MMSE channel estimators.

The pairwise error probability (PEP) for multiple- input multiple-output (MIMO) radio interfaces is investigated by means of a novel formulation based on compound matrices. The proposed approach is suitable for any MIMO system having average upper-bounded PEP written as$[\det ( {\mmb I}+\gamma \, {\mmb A})]^{-\zeta }$, where$ {\mmb A}$is a Hermitian matrix,$\zeta $an integer number, and$\gamma $the signal-to-noise ratio (SNR); that bound frequently results in MIMO single- and multicarrier transmissions. It is shown that the minimization of the bounded PEP should consider the whole set of nonzero compound matrices of$ {\mmb A}$. In particular, the SNR of interest marks the compound matrix that mainly drives the system performance. Both diversity advantage and coding gain are given as continuous functions of the variable$\gamma $, hence, their asymptotic behaviors are taken as important case of studies. The interaction effects between channel code and propagation environment are also discussed. It is shown how the eigenvectors and eigenvalues of the autocorrelation channel matrix may be considered for code design. It is also proved the maximization of the code rank is not always a necessary requirement for performance improvement being its optimal value fixed by the channel structure and SNR of interest. Finally, the analysis is applied to space–time trellis-coded transmissions over spatially correlated slow Rayleigh-fading channels.

In this paper, we study the effects of phase noise in a multicarrier system. We show that the phase noise introduces interference components, and we evaluate their second order statistics. The analysis is then applied to two particular multicarrier architectures: discrete multitone (DMT) modulation, and filtered multitone (FMT) modulation. For the DMT case, our results are in agreement with other related work that considers DMT only. For FMT modulation, we demonstrate that the phase noise power spectral density as well as the frequency response of the prototype filters play a key role on the resulting interference power value. Finally, we show that the analytical performance results are close to the simulation results.