Abstract — The presence of both multiple-access interference (MAI) and intersymbol interference (ISI) constitutes a major impediment to reliable communications in multipath code-division multiple-access (CDMA) channels. In this paper, an iterative receiver structure is proposed for decoding multiuser information data in a convolutionally coded asynchronous multipath DS-CDMA system. The receiver performs two successive softoutput decisions, achieved by a soft-input soft-output (SISO) multiuser detector and a bank of single-user SISO channel decoders, through an iterative process. At each iteration, extrinsic information is extracted from detection and decoding stages and is then used as a priori information in the next iteration, just as in Turbo decoding. Given the multipath CDMA channel model, a direct implementation of a sliding-window SISO multiuser detector has a prohibitive computational complexity. A low-complexity SISO multiuser detector is developed based on a novel nonlinear interference suppression technique, which makes use of both soft interference cancellation and instantaneous linear minimum mean-square error filtering. The properties of such a nonlinear interference suppressor are examined, and an efficient recursive implementation is derived. Simulation results demonstrate that the proposed low-complexity iterative receiver structure for interference suppression and decoding offers significant performance gain over the traditional noniterative receiver structure. Moreover, at high signal-to-noise ratio, the detrimental effects of MAI and ISI in the channel can almost be completely overcome by iterative processing, and single-user performance can be approached. Index Terms — Coded CDMA, instantaneous MMSE filtering, multiuser detection, soft interference cancellation, Turbo processing.

Since the invention of \turbo codes" by Berrou et al. in 1993, the \turbo principle" has been adapted to several communication problems such as \turbo equalization", \turbo trellis coded modulation", and iterative multi user detection. In this paper we study the \turbo equalization" approach, which can be applied to coded data transmission over channels with intersymbol interference (ISI). In the original system invented by Douillard et al., the data is protected by a convolutional code and a receiver consisting of two trellis-based detectors are used, one for the channel (the equalizer) and one for the code (the decoder). It has been shown that iterating equalization and decoding tasks can yield tremendous improvements in bit error rate (BER). We introduce new approaches to combining equalization based on linear ltering with the decoding. The result is a receiver that is capable of improving BER performance through iterations of equalization and decoding in a manner similar to turbo ...

Abstract—A number of important advances have been made in the area of joint equalization and decoding of data transmitted over intersymbol interference (ISI) channels. Turbo equalization is an iterative approach to this problem, in which a maximum a posteriori probability (MAP) equalizer and a MAP decoder exchange soft information in the form of prior probabilities over the transmitted symbols. A number of reduced-complexity methods for turbo equalization have recently been introduced in which MAP equalization is replaced with suboptimal, low-complexity approaches. In this paper, we explore a number of low-complexity soft-input/soft-output (SISO) equalization algorithms based on the minimum mean square error (MMSE) criterion. This includes the extension of existing approaches to general signal constellations and the derivation of a novel approach requiring less complexity than the MMSE-optimal solution. All approaches are qualitatively analyzed by observing the mean-square error averaged over a sequence of equalized data. We show that for the turbo equalization application, the MMSE-based SISO equalizers perform well compared with a MAP equalizer while providing a tremendous complexity reduction. Index Terms—Equalization, iterative decoding, low complexity, minimum mean square error. I.

We study the convergence behavior of iterative decoding of a serially concatenated code. We rederive a existing analysis technique called EXIT chart [15] and show that for certain decoders the construction of an EXIT chart simplifies tremendously. The findings are extended such that simple irregular codes can be constructed, which can be used to improve the converence of the iterative decoding algorithm significantly. An efficient and optimal optimization algorithm is presented. Finally, some results on thresholds on the decoding convergence are outlined.

Abstract—In this paper, we consider soft decision directed channel estimation for turbo equalization. To take advantage of soft information provided by the decoder, a minimum mean square error linear channel estimator is derived under an uncorrelated channel tap model, and a soft input recursive least squares algorithm is also developed by modifying the cost function of the conventional recursive least squares algorithm. The performance of the proposed channel estimators are analyzed in terms of mean square identification error (MSIE) for stationary channels. Simulation results for both time-invariant and time-varying frequency-selective Rayleigh fading channels are also presented. Index Terms—Channel estimation, soft input, turbo equalization. I.

A number of important advances have been made in the area of joint equalization and decoding of data transmitted over intersymbol interference channels. Turbo equalization is an iterative approach to this problem, in which a maximum a-posteriori probability (MAP) equalizer and a MAP decoder exchange soft information in the form of prior probabilities over the transmitted symbols. A number of reduced-complexity methods for turbo-equalization have recently been introduced in which MAP equalization is replaced with sub-optimal, low-complexity approaches. In this paper, we explore a number of low-complexity soft-input/soft-output (SISO) equalization algorithms based on the minimum mean square error (MMSE) criterion. This includes the extension of existing approaches to general signal constellations and the derivation of a novel approach requiring less complexity than the MMSE-optimal solution. All approaches are qualitatively analyzed by observing the mean-square error averaged over a sequence of equalized data. We show that for the turbo equalization application the MMSE-based SISO equalizers perform well compared to a MAP equalizer while providing a tremendous complexity reduction.

For coded data transmission over channels introducing inter-symbol interference (ISI), Douillard et al. proposed the \turbo equalization" approach, which is an iterative equalization and decoding algorithm for the receiver able to almost entirely compensate for the system performance degradation, e.g., in the bit error rate, due to ISI. One drawback of this approach is the exponentially increasing (in the ISI channel response length) complexity of the equalization step. To overcome this, several equalizers applicable to \turbo equalization" requiring reduced computational complexity have been proposed. We rederive some of these approaches and equalize the received data in the frequency domain. Besides the promising performance results, the new algorithms provide a further complexity reduction compared to the time domain counterparts. 1 Introduction Many communication systems today encounter the problem of data transmission over a channel with inter-symbol interference (ISI). To protec...

We consider the problem of joint equalization and decoding, using the method of turbo equalization originally developed by Douillard, et al. [3]. In its original form, turboequalization requires accurate knowledge of the channel at the receiver. We propose a receiver structure, based on a soft-input Kalman channel estimator, that can operate effectively without accurate channel knowledge and without training data. The resulting joint channel and data estimator is shown to outperform standard turbo equalization based on moderate-length traning data. 1.

Recent advances in information theory and terrestrial wireless communication show that significant performance gains are achievable with increased signaling diversity through the use of multiple transmit and receive arrays. An effective approach for increasing data rate over wireless channels is to employ coding techniques appropriate for multiple transmit antennas, namely space-time coding. This paper investigates the feasibility and effectiveness of space-time trellis and layered space-time codes for the shallow-water, acoustic, frequencyselective channel. Using data collected during a recent experiment in the Mediterranean, we show that systems using multiple transmit and receive transducers outperform more conventional single-transmit single-receiver configurations.

This paper addresses the design and performance evaluation of a pilot-aided turbo-coded system to achieve reliable PSK communication in frequencyflat, time-selective fading, with a relatively high Doppler rate. We introduce a suitable Markov model with a finite number of states, designed to approximate both the values and the statistical properties of the correlated flat fading channel phase, which poses a more severe challenge to PSK transmission than amplitude fading. The Forward-Backward algorithm is used to determine both the maximum a posteriori probability (MAP) value for each bit in the data sequence, and the MAP channel phase in each iteration. Soft information is exchanged between the phase and data estimation modules. Using a turbo-code and joint iterative decoding and channel estimation, performance is demonstrated to approach an upper bound to the capacity of a Markov-phase channel. I. Introduction Recently researchers have been exploring ways to utilize the enormous poten...