Markov characterization of channels with soft decision outputs

It is shown that a methodology based on hidden Markov models is applicable to the modeling of slowly varying Rayleigh fading channels with additive Gaussian noise and soft decision outputs. The fading is considered to be frequency nonselective, and ideal demodulation is considered throughout. To prove the validity of robustness of the modeling technique, various results that show good agreement between the simulated channels and the models found are presented. Two soft decision statistical distributions, namely, the soft burst and soft burst interval distributions, are defined and compared. To illustrate the accuracy of the models obtained, the simulation and model outputs are compared for a convolutional encoder with Viterbi decoding and various degrees of interleaving     

A reduced-complexity frequency detector derived from themaximum-likelihood principle

In addition to the conventional matched filter, implementation of Gardner's (1990) frequency detector (GFD) based on the maximum-likelihood principle also involves a frequency-matched filter. the present authors give a reduced-complexity frequency detector (RCFD) derived from Gardner's detector through a simple approximation to the frequency-matched filter. They analyze its steady-state jitter properties and frequency acquisition performance and compare it to the original GFD as well as to the well-known balanced quadricorrelator. The results show that performance of the RCFD is close to that of the original GFD and that this detector significantly outperforms the balanced quadricorrelator which involves a similar hardware complexity. Another contribution of the paper is the analysis of GFD and RCFD when their outputs are computed at twice the symbol rate     

A reduced-complexity online state sequence and parameter estimatorfor superimposed convolutional coded signals

This paper develops a reduced-complexity online state sequence and parameter estimator for superimposed convolutional coded signals. Joint state sequence and parameter estimation is achieved by iteratively estimating the state sequence via a variable reduced-complexity Viterbi algorithm (VRCVA) and the model parameters via a recursive expectation maximization (EM) approach. The VRCVA is developed from a fixed reduced-complexity Viterbi algorithm (FRCVA). The FRCVA is a special case of the delayed decision-feedback sequence estimation (DDFSE) algorithm. The performance of online versions of the FRCVA, VRCVA, and the standard Viterbi algorithm (VA) are compared when they are used to estimate the state sequence as part of the reduced-complexity online state sequence and parameter estimator     

A high-performance reduced-complexity GMSK demodulator

A four-state adaptive maximum-likelihood sequence estimation (MLSE) Gaussian minimum-shift keying (GMSK) demodulator for modulation (BT=0.3) on AWGN channels is analyzed and simulated. This demodulator uses the linear representation of GMSK signals and achieves near-optimum BER performance. The channel-impulse response used in the MLSE demodulator is initialized to the highest energy component in the linear representation, and then adapted in a decision-directed mode to offset any performance losses incurred by initially ignoring other lower energy (and time-varying) components in the linear representation. The number of MLSE states is reduced to two, at about 0.1-dB performance loss, by implementing a whitening matched filter which concentrates most of the GMSK pulse energy in its two leading samples     

A bandwidth-optimized reduced-complexity equalized multicarriertransceiver

A bandwidth-optimized and equalized multicarrier transceiver that achieves near-optimum performance at a practical complexity level is described. The equalizer used is a relatively short FIR filter whose taps and delay are set to optimize the performance of the multicarrier transceiver. Simulation results on a set of carrier-serving-area digital subscriber loops are also presented to demonstrate the separate and joint effects of bandwidth optimization and equalization on performance. Finally, the intriguing idea of using a pole-zero equalizer to achieve the high performance of long FIR equalizers at a much lower implementation cost is investigated     

A reduced-complexity algorithm for combined equalization and decoding

This paper presents a new application of a suboptimal trellis decoding algorithm for combined equalization and decoding. The proposed algorithm can outperform the reduced-state sequence estimator (RSSE) of the same order of complexity. The algorithm, termed estimated future decision-feedback algorithm (EFDFA), was originally proposed for the problem of noncoherent decoding with multiple-symbol overlapped observations and is now reformulated for the problem of intersymbol interference inflicted channels. The EFDFA uses the RSSE as a building block. The performance improvement is achieved by using estimated future symbols in the decision process. The estimated future symbols are obtained by RSSE decoding time-reversed blocks of the input. The same technique can be used to greatly enhance the performance of the conventional decision-feedback equalizer. An analysis of the performance of the EFDFA based on the performance of the RSSE is described. The EFDFA can be configured as an adaptive equalizer capable of operating in a time-varying environment, and is shown to perform well in fading conditions. With only minor additional complexity, the EFDFA is also capable of producing soft outputs.     

A reduced-complexity maximum-likelihood method for multiuser detection

Multiuser or joint detection has recently been receiving significant research interest because of its potential for the significant increase in system capacity and performance. Among the conventional multiuser detection schemes such as the minimum mean-squared error and successive interference cancellation-based ones, maximum-likelihood multiuser detection has the best system performance. However, the complexity of maximum-likelihood detection (MLD) increases exponentially in the number of users and constellation size. In this paper, a low-complexity MLD scheme based on the use of a sensitive-bits algorithm is proposed. It is demonstrated that the proposed method can greatly reduce the computational complexity with a minimal penalty in performance compared with the exhaustive optimal MLD scheme.     

A novel tunable-complexity turbo-soft detector for high-throughputHDSL applications over ISI channels with crosstalk

We present a novel iterative (turbo) receiver with tunable complexity for reliable detection of (uncoded) payload data transmitted over long intersymbol interference (ISI) channels affected by crosstalk, as those typically encountered in emerging HDSL2 services standardized by ANSI T1.418 recommendation. The proposed receiver combines in an original way "minimum mean square error (MMSE) estimation principle," "turbo-processing principle" and "crosstalk-prediction principle" for achieving both suboptimal maximum a posteriori probability channel equalization and reliable soft-mitigation of ISI tail plus crosstalk. More in detail, according to the turbo-processing principle, at each iteration suitable extrinsic information is extracted from the equalizing and interference-canceling modules and used as "a priori information" for the next iteration. Several simulation results on typical HDSL-like test-loops confirm the superiority of the proposed turbo-detector (TD) over current solutions based on conventional MMSE decision-feedback equalizers and precoders (such as the Tomlinson-Harashima precoder). The numerical tests also point out that performance of the presented TD on typical high bit-rate digital subscriber lines (HDSL) is not limited by error-floor phenomena, even at error-probabilities below 10^-7     

A new reduced-complexity algorithm for multiple-symbol differential detection

Although multiple-symbol differential detection (MSDD) provides much better error performance than conventional differential detection, its complexity is much higher. We propose a reduced-complexity algorithm for MSDD. Our simulations show that the performance of the reduced complexity receiver is nearly identical to that of the original receiver, but with typical parameters, the number of phase sequences searched can be reduced by a factor of sixteen for QPSK to over four thousand for 16-PSK thereby leading to significant receiver complexity reduction.     

A BEM-based detector for CPM signals transmitted over frequency-flat fading channels

In this paper, the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation of a random vector is applied to the problem of symbol detection for continuous phase modulation signals transmitted over time-selective Rayleigh-fading channels. This results in a soft-in-soft-out detection algorithm suitable for iterative detection/decoding schemes. Simulation results show that the error performance provided by the proposed solution is very close to that of a MAP detector endowed with an ideal knowledge of the channel state both in uncoded and coded transmissions.     

A blind detector for Rayleigh flat-fading channels with non-Gaussian interference via the particle learning algorithm

A blind particle learning detector (BPLD) is developed for signal detection in Rayleigh flat-fading channels with non-Gaussian interference. The parameters of the fading channel model and the noise model are all unknown. The impulsive noise is modeled as a mixture of Gaussian distributions, which is capable of representing a broad class of non-Gaussian noise. The particle learning algorithm is employed to simultaneously estimate signal and parameters of the fading channel model and the noise model. The delay weight method is used to improve the performance. Simulation results show that the performance of the BPLD proposed can follow closely the performance of the detector with known parameters of the fading channel model and the noise model.     

Receiver with iterative soft decision for frequency selectivefading channels

The authors propose a novel receiver which makes an iterative soft decision based on an a posteriori probability criterion with forward-error-corrected data. The proposed receiver significantly improves the bit error rate after forward-error-correction in selective fading channels     

A reduced-complexity image coding scheme using decision-directed wavelet-based contourlet transform

Recently the wavelet-based contourlet transform (WBCT) is adopted for image coding because it matches better image textures of different orientations. However, its computational complexity is very high. In this paper, we propose three tools to enhance the WBCT coding scheme, in particular, on reducing its computational complexity. First, we propose short-length 2-D filters for directional transform. Second, the directional transform is applied to only a few selected subbands and the selection is done by a mean-shift-based decision procedure. Third, we fine-tune the context tables used by the arithmetic coder in WBCT coding to improve coding efficiency and to reduce computation. Simulations show that, at comparable coded image quality, the proposed scheme saves over 92% computing time of the original WBCT scheme. Comparing to the conventional 2-D wavelet coding schemes, it produces clearly better subjective image quality.     

A robust detector for multicarrier spread spectrum transmission over partially jammed channels

Multicarrier spread spectrum (MC-SS) is an alternative to the conventional spread spectrum (SS) techniques that behave significantly better when the system is subject to narrow- or partial-band interference. However, successful implementation of the optimum detector requires knowledge of noise and interference variance in each subcarrier band. We propose a suboptimal detector for MC-SS that keeps the significant gain of MC-SS over the conventional SS, with a relatively low loss compared with the optimum MC-SS detector. Theoretical analysis and computer simulations that corroborate the theory are presented.     

A blind particle filtering detector of signals transmitted over flat fading channels

A new particle filtering detector (PFD) is proposed for blind signal detection over flat Rayleigh fading channels whose model coefficients are unknown. The detector employs a hybrid importance function and a mixture Kalman filter. It also incorporates an auxiliary particle filtering strategy with a smoothing kernel in the resampling step. Further, by considering practical information of communication systems and the physical interpretation of the adopted second-order autoregressive (AR) channel model, a fully blind particle filtering implementation is developed. The structure of the proposed PFD can be easily adapted to other system requirements. Simulations are provided that demonstrate the performance of the new PFD.     

New adaptive Viterbi detector for fast-fading mobile-radio channels

A new method to significantly increase the performance of an adaptive Viterbi detector used on fast-fading mobile-radio channels is presented. The proposed method, which uses a predictor to counter the delay of the channel estimates, may reduce the bit error rate for high SNR by up to 90%.<>     

Turbo equalization with sequential sequence estimation overmultipath fading channels

We investigate the performance of a turbo equalization scheme over frequency-selective fading channels, where a soft-output sequential algorithm is employed as the estimation algorithm. The advantage of this scheme comes from the low computational complexity of the sequential algorithm, which is only linearly dependent on the channel memory length. Simulation results of an 8-PSK trellis-coded modulation (TCM) system show that the performance of this scheme suffers approximately 2-dB loss compared with that of the turbo max-log maximum a posteriori (MAP) probability equalizer after 5 iterations     

The mean-square delayed decision feedback sequence detector

In signal equalization, a detection technique that allows reduction of the number of states of the Viterbi (1979) detector is the delayed decision feedback sequence detector (DDFSD). In order to achieve good performance, it is crucial to operate an appropriate prefiltering of the received sequence before the DDFSD. The main novelty of the paper is performance evaluation of the DDFSD when the feedforward filter of the minimum mean-square error decision feedback equalizer (DFE) is adopted as prefilter. The union upper bound is used to evaluate the probability of first error event and truncation of the sum appearing in the bound to the error sequences that dominate the performance is discussed. It is also shown that the feedforward filter of the minimum mean-square error DFE leads to maximum likelihood sequence detection with a minimum number of states, which seems to be a novel result.     

Iterative multiuser detection with Gauss-Seidel soft detector asfirst stage for asynchronous coded CDMA

A new iterative detection and decoding structure for asynchronous convolutionally coded and turbo coded CDMA is proposed. The new scheme is based on the combination of Gauss-Seidel soft detection and parallel interference cancellation. The complexity of the new scheme is linear to the number of users. It is shown that for a heavily loaded system, near-optimal performance can be obtained