In the above titled paper (ibid., vol. 49. no.9, pp. 2309-2313, Sep 03), several equations were printed incorrectly. The corrections are presented.

Space-time coding techniques are methods to improve transmission efficiency in radio channels by using multiple transmit and/or receive antennas. Bit-interleaved coded modulation gives good diversity gains with higher order modulation schemes using well known binary convolutional codes. By using orthogonal frequency division multiplexing (OFDM), wideband transmission can be achieved over frequency selective fading radio channels. In this paper, we combine these three ideas into a family of flexible space-time coding methods where modulation order and coding rates as well as the number of transmit and receive antennas can be changed without drastic redesign of the space-time codes. Near optimum iterative decoders are evaluated in terms of simulation for slowly varying wireless LAN type channels. Significant performance gains over the 802.11a standard system are reported.

We propose a new linear multiuser receiver for synchronous code-division multiple-access (CDMA) systems, referred to as the orthogonal multiuser (OMU) receiver. Unlike the linear minimum mean-squared error (MMSE) receiver, the OMU receiver depends only on the signature vectors and does not require knowledge of the received amplitudes or the channel signal-to-noise ratio (SNR). Several equivalent representations of the receiver are developed with different implications in terms of implementation. In the first, the receiver consists of a decorrelator demodulator followed by an optimal whitening transformation on a space formed by the signatures. In the second, the receiver consists of a bank of correlators with correlating vectors that are projections of a set of orthogonal vectors, and are closest in a least squares sense to the decorrelator vectors and also closest in a least squares sense to the signature vectors. In the third, the receiver consists of a single-user matched filter (MF) followed by an optimal whitening transformation on a space formed by the signatures. We derive exact and approximate expressions for the probability of bit error, as well as the asymptotic signal-to-interference+noise ratio (SINR) in the large system limit. The analysis suggests that over a wide range of channel parameters the OMU receiver can outperform both the decorrelator and the single-user MF and perform similarly to the linear MMSE receiver, despite not knowing the channel parameters.

We derive the asymptotic signal-to-interference ratio (SIR) of the decorrelator in the large system limit, both for the case in which the number of users exceeds the spreading gain and for the case in which the number of users is less than the spreading gain. We show that, contrary to what is claimed by Tse and Hardy (see ibid., vol.45, p.641-57, Mar. 1999) and by Tse and Zeitouni (see ibid., vol.46, p.171-188, Jan. 2000) when the number of users exceeds the spreading gain and the decorrelator is defined in terms of the Moore-Penrose pseudoinverse, the SIR does not converge to zero.

We propose a new low-complexity strategy for approaching the matched filter bound on most practical ISI channels. At the transmitter, a form of channel-independent precoding is introduced to perform frequency-interleaving, which appropriately conditions the channel. At the receiver, a very low-complexity iterated-decision equalizer (Chan, A.M. and Wornell, G.W., IEEE Trans. Commun., vol.49, p.1966-76, 2001) is used. As an illustration, we use the system to attain the matched filter bound effectively on the 1+D channel without the use of error-control coding.

Sphere decoding for multiple antenna systems has been shown to achieve near-ML performance with low complexity. However, the achievement of such an excellent performance-complexity tradeoff is highly dependent on the initial choice of sphere radius. We present a new sphere decoding algorithm which is even less computationally complex than the original sphere decoder. Moreover, the complexity of the new sphere decoder is relatively insensitive to the initial choice of sphere radius. Thus, by making the choice of radius sufficiently large, the ML solution is guaranteed with low complexity, even for large constellations. In our simulations, we show that with 4 transmit and 4 receive antennas and 64-QAM, our new sphere decoding algorithm achieves the exact ML solution with approximately a factor of 3.5 reduction in complexity when compared to the original sphere decoder, and a factor of 10/sup 5/ reduction when compared to brute-force ML decoding.

A promising class of nonlinear multiuser detector is introduced for CDMA systems. These "iterated-decision" multiuser detectors use optimized multipass algorithms to successively cancel multiple-access interference (MAI) from received data and generate symbol decisions whose reliability increases monotonically with each iteration. They significantly outperform decorrelating detectors and linear minimum mean-square error (MMSE) multiuser detectors, but have the same order of computational complexity. When the ratio of the number of users to the spreading factor is below a certain threshold, iterated-decision multiuser detectors asymptotically achieve the performance of the "optimum" multiuser detector, ie, maximum-likelihood (ML) decoding.

A new and efficient class of nonlinear equalizers is developed for intersymbol interference (ISI) channels. These -"iterated-decision equalizers" use an optimized multipass algorithm to successively cancel ISI from a block of received data and generate symbol decisions whose reliability increases monotonically with each iteration. These equalizers have an effective complexity comparable to the decision-feedback equalizer (DFE), yet asymptotically achieve the performance of maximum-likelihood sequence detection (MLSD). We show that, because their structure allows cancellation of both precursor and postcursor ISI, iterated-decision equalizers outperform the minimum mean-square error DFE by 2.507 dB on severe ISI channels even with uncoded systems. Moreover, unlike the DFE, iterated-decision equalizers can be readily used in conjunction with error-control coding, making them attractive for a wealth of applications.

A new and efficient class of nonlinear equalizers is introduced for intersymbol interference (ISI) channels. These "iterated-decision equalizers" use an optimized multipass algorithm to successively cancel ISI from a block of received data and generate symbol decisions whose reliability increases monotonically with each iteration. Asymptotically they achieve the performance of maximum-likelihood sequence detection (MLSD), but only have a computational complexity on the order of a linear equalizer (LE). And because their structure allows cancellation of both pre- and post-cursor ISI, iterated-decision equalizers outperform the minimum mean-square error decision-feedback equalizer (DFE) by 2.5 dB on severe ISI channels even with uncoded systems. Even more importantly, unlike the DFE, iterated-decision equalizers can be readily used in conjunction with error-control coding, making them attractive for a wealth of applications.

We present the minimum phase space volume (MPSV) technique, a nonlinear dynamical technique for enhancing speech and audio signals corrupted by convolutional noise. The MPSV technique requires no assumptions or a priori information about the original signal, remains effective when the inverse filter order is overestimated, and significantly outperforms the LS method.

Acoustical room reverberations can be removed from speech signals by using inverse filtering, which is basically a blind deconvolution approach. Blind deconvolution usually recovers the original speech signal with the use of statistics. However, research shows that the speech generation process is not truly random. In this paper, we propose an inverse filtering technique called minimum phase space volume (MPSV), which exploits the dynamics rather than the statistics of speech signals. Furthermore, the MPSV technique does not require any statistical information whatsoever about the original speech signal. We compare the performance of the MPSV technique to that of the LS technique in deconvolving a speech signal and find that the MPSV technique consistently outperforms the LS technique.