In most wireless communications research, the channel models considered experience less severe fading than the classic Rayleigh fading case. In this work, however, we investigate MIMO channels where the fading is more severe. In these environments, we show that the coefficient of variation of the channel amplitudes is a good predictor of the link mutual information, for a variety of models. We propose a novel channel model for severely fading channels based on the complex multivariate t distribution. For this model, we are able to compute exact results for the ergodic mutual information and approximations to the outage probabilities for the mutual information. Applications of this work include wireless sensors, RF tagging, land-mobile, indoor-mobile, ground-penetrating radar, and ionospheric radio links. Finally, we point out that the methodology can also be extended to evaluate the mutual information of a cellular MIMO link and the performance of various MIMO receivers in a cellular scenario. In these cellular applications, the channel itself is not severely fading but the multivariate t distribution can be applied to model the effects of inter-cell interference.

We propose an approximation of the line spectral frequencies (LSFs) to the LPC cepstral coefficients (LPCCs). A direct relationship between LPCC and LSF is derived, and new parameters, named pseudo-cepstral coefficients, are obtained by reducing some factors in the relationship. We also address the statistical properties of the pseudo-cepstral coefficients and show their useful application to speech recognition.

In digital communication networks, a speech recognition system extracts feature parameters after reconstructing speech signals. In this paper, we consider a useful approach of incorporating speech coding parameters into a speech recognizer. Most speech coders employ line spectrum pairs (LSPs) to represent spectral parameters. We introduce weighted distance measures to improve the recognition performance of an LSP-based speech recognizer. Experiments on speaker-independent connected-digit recognition showed that weighted distance measures provide better recognition accuracy than unweighted distance measures do. Compared with a conventional method employing mel-frequency cepstral coefficients, the proposed method achieved higher performance in terms of a recognition accuracy.